**15. Harvest**

Cassava is due for harvesting any time from 6 month. The crop does not have a specific time or season for its harvest; it can be harvested all-year-round. The fact that it can stay long and be preserved in the soil gives it the utmost advantage of being harvested piecemeal over a long period. The root is cooked and consumed as a local delicacy. Also, it could be processed to give a varieties of products (**Figures 6**–**8**) as a result of value addition. Cassava leaves can be fed upon as vegetable, and it is used as such in many homes where they are planted in West African countries. Moreover, cassava leaves and root serve as a good source of nutrients for livestock. The leaves are rich in vitamins.

**Figure 6.** *Cassava coarse (grains) flour.*

**Figure 7.** *Cassava (smooth) flour.*

**Figure 8.** *Cassava end (food) products.*

#### *Cassava Production Enterprise in the Tropics DOI: http://dx.doi.org/10.5772/intechopen.104677*

Cassava has a number of advantages, one of which is, it does not have a set harvesting season. They can be collected whenever needed during times of food scarcity, frequently one plant or even one root at a time. Harvesting for human consumption takes roughly 8–10 months; for industrial purposes, a longer growth time yields a higher root and starch output. Roots can be eaten directly by farm families, given to livestock, or sold for processing into a wide range of value-added products, from coarse flour ('Garri')to high-tech modified starch gels. The root of the plant is not the only portion that can be useful. The green section of the upper stem, which includes the leaves and petioles, is fed to cattle and buffaloes in several countries, while the leaf blades are fed to pigs and chickens. Fresh leaves are used to raise silkworms in China, Thailand, and Vietnam. Woody stems are crushed up and used as a substrate for growing mushrooms. Stumps are burned as fuelwood [12].

Cassava roots are typically collected by cutting the stems approximately 20 cm above ground and then dragging the entire root system out of the ground using the stump. If the soil is too hard or the roots are too deep, it may be necessary to remove the soil around the roots with a hoe, spade, or pick while avoiding injury to the roots. A harvesting blade mounted to a tractor is occasionally employed in heavy soils that can become quite hard in the dry season. The sword slashes through the soil material. The tractor's forward momentum pulls the root clusters to the surface as the blade slices through the dirt right below the roots. The roots are then removed from the stump and transported in baskets or bags.

Large cassava fields are frequently harvested by middlemen who employ teams of labourers and deliver the roots to marketplaces or processing plants via trucks. Plant tops are harvested after the root harvest. Plant tops are generally left to dry on the ground after root harvesting and then integrated into the soil to help preserve its fertility. However, by trimming the green tops every 3 months during the plant's growth cycle, farmers can considerably increase the total amount of cassava foliage available for feeding to animals. Within 2–3 months after each trimming, the remaining stems will sprout and produce a new crop of leaves. Cassava stakes should be planted at a closer spacing of about 60 × 60 cm for maximum foliage output. Young leaves harvested at regular intervals during the cassava growth cycle have a higher protein and lower fiber content than those gathered at the end of the cassava growth cycle, when plants are generally harvested between 11 and 12 months. Younger leaves are more pleasant and give better nutrition.

The ultimate root production decreased as the frequency of leaf cutting increased, from roughly 40 tonnes per ha when leaves were collected only once at the time of root harvest to less than 25 tonnes when leaves were removed 5 times [27]. This approach may or may not be cost-effective, depending on labour costs and the relative pricing of fresh roots and dry leaves. Harvesting the plant tops four or five times over a one-year growth cycle takes a substantial amount of nutrients, particularly nitrogen, from the field, and would be unsustainable unless large amounts of mineral fertilizer were applied to maintain soil fertility.


*Article* **Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction**

**Ning Gui 1, Jieli Lou 2, Zhifeng Qiu 3,\* and Weihua Gui 3**

Received: 27 March 2019; Accepted: 5 June 2019; Published: 22 July 2019

**Abstract:** Accurately predicting the reheater steam temperature over both short and medium time periods is crucial for the efficiency and safety of operations. With regard to the diverse temporal effects of influential factors, the accurate identification of delay orders allows effective temperature predictions for the reheater system. In this paper, a deep neural network (DNN) and a genetic algorithm (GA)-based optimal multi-step temporal feature selection model for reheater temperature is proposed. In the proposed model, DNN is used to establish a steam temperature predictor for future time steps, and GA is used to find the optimal delay orders, while fully considering the balance between modeling accuracy and computational complexity. The experimental results for two ultra-super-critical 1000 MW power plants show that the optimal delay orders calculated using this method achieve high forecasting accuracy and low computational overhead. Moreover, it is argued that the similarities of the two reheater experiments reflect the common physical properties of different reheaters, so the proposed algorithms could be generalized to guide temporal feature selection for other reheaters.

**Keywords:** reheat steam temperature; temporal feature selection; delay order prediction; deep neural network; genetic algorithm

#### **1. Introduction**

Steam reheating plays an important role in power plants. It can increase thermal efficiency by 2% and it can also reduce steam humidity and improve the safety of the final stage's blade [1,2]. However, due to the complexity of the many influential factors, it is difficult to maintain the reheat steam temperature within a certain range [3]. For instance, the reheater steam temperature of two ultra-super-critical 1000 MW units investigated in this paper may fluctuate between 565 ◦C and 610 ◦C, while the normal reheater outlet steam temperature is 603 ◦C with tolerable fluctuation within the range of 503 to 608 ◦C [4] (the specific threshold may vary with the type of reheater). A temperature that is too high will cause damage to the metal material, while a temperature that is too low will reduce the thermal cycle efficiency [5]. Therefore, finding features that affect the modeling target and analyzing the extent of these features are crucial for the system's safety and efficiency.

A reheater system is a typical nonlinear hysteresis thermal system, which is highly coupled, complex, and impacted by many factors [6,7]. The selection of the most related features from a large variety of sensors is important for the realization of effective control [8]. Traditional feature selections are normally developed on the basis of mass balance, energy balance, and dynamic principles, which rely greatly on human expertise and normally require a long modeling time [9–11]. Recently, researchers have increasingly adopted the data-driven methodology that extracts features directly from huge amounts of accumulated process data [12–14]. Li et al. [15] analyzed operation parameters in power plants by correlation analysis to improve boiler efficiency. Wei et al. [16] used principle

component analysis to transform higher-dimensional original data to lower-dimensional principle components, which were employed as the inputs to the *NOx* emission model to reduce memory storage requirements and computational costs for data analytics. Buczy ´nski et al. [17] judged whether features could exert substantial effects on a CFD (phase fluidised bed)-based model using sensitivity analysis to predict the performance of a domestic central-heating boiler fired with solid fuels. Pisica et al. [18] chose mutual information to assess the relevance of feature subsets in order to determine the operating states of power systems. Wang et al. [19] utilized the outputs of an improved random forest algorithm as inputs of a back propagation neural network to weight the importance of features and to improve the prediction accuracy of *NOx*.

The above research works mainly focused on finding the most related features with respect to the modeling target, which only explores one dimension from all possible relationships. In practice, for the complex process, each feature may have a temporal effect on the modeling target [20]. For instance, some features might have a rapid impact on the target, while some other features might only display certain time-delay effects, i.e., effects after a certain period of time. In order to cover the temporal effects, multi-step features are often accumulated for data-driven modeling in the feature engineering process. Normally, the larger the delay order (number of steps selected) of a feature is, the more information it contains [21]. However, overly large delay orders of features may lead to overfitting, which may cause poor performance on unseen instances [22] and significantly increase memory storage and computational complexity for data analysis [23]. Therefore, it is necessary to find an optimal delay order set for each feature while maintaining a good balance between modeling accuracy and computational economy.

A few researchers have investigated the temporal feature selection problem. Lv et al. [21] used particle swarm optimization to determine delay orders and used a least square support vector machine (SVM) to predict the bed temperature of circulating fluidized bed boilers. However, this method suffered from computational complexity when modeling large-scale data sets. Shakil et al. [24] applied genetic algorithms to estimate the time delay of soft sensors for *NOx* and *O*2. Although these studies achieved good results on the delay order selection, their modeling targets were only for one particular future time instance, which has the potential not to include the features that impose too rapid or too slow impacts on the target. These approaches also provided little discussion on whether the generated delay order could be used to guide future modeling processes for similar equipment.

To address the optimal feature selection of delay orders for multi-step prediction, a method that combines a deep neural network (DNN) and a genetic algorithm (GA) is proposed. A prediction target with multiple future time steps is introduced to explore features that have rapid or slow effects. A DNN model is used to establish a steam temperature predictor [25] for the next 20 steps. A GA is proposed to find the optimal delay orders with the objective function of balancing modeling accuracy and computational complexity. The proposed method is tested in two 1000 MW coal-fired power plants, namely unit 3 and unit 4, which use more than two million records. The results of the two units display similar sets of delay orders for each feature, reflecting that the physical properties of reheater steam systems are similar to some extent.

The rest of this paper is organized as follows: Section 2 briefly describes the reheater system and proposes the problem statement. Section 3 establishes an objective function for model evaluation. The detailed introduction of the delay order selection mechanism is provided in Section 4. Section 5 presents experiments and discussions. Discussions and possible directions for future work are provided in the final section.

#### **2. System Description and Problem Statement**

#### *2.1. Description of Reheater System*

A reheater is a set of tubes located in a boiler, the main purpose of which is to avoid excess moisture in steam at the end of expansion to protect the turbine. The exhaust steam from the high-pressure turbines passes through these heated tubes to collect more energy before driving the intermediate- and then low-pressure turbines. The conceptual structure of the reheater unit is shown in Figure 1.

**Figure 1.** Reheater structure.

After the high-pressured turbine, the exhaust pressure and temperature at the inlet of the reheater are about 35–37 kg/cm2 and 345–355 ◦C, respectively. A reheater is designed in the shape of a serpentine tube in order to increase the heated area. The hot smoke generated by the combustion of coal transfers heat to the reheater, meaning that the temperature of steam in the reheater rises. The steam temperature at the outlet of the reheater is kept around 603 ◦C. Reheater steam with high-temperature and high-pressure characteristics is collected into the high-temperature reheat steam container. A similar process is performed again in the low-pressure cylinder.

Table 1 denotes the influential features of our modeling target, which is the outlet steam temperature of the reheater. Many features affect the reheat steam temperature, such as the inlet steam temperature, inlet gas temperature, smoke baffle opening, etc. Also, these variables have different inertias toward the reheater outlet steam temperature. Therefore, these variables and their hysteresis times should be considered in the prediction model. Here, the previous values of the steam outlet temperature are also used in the modeling process and the multi-step steam temperatures are used as the outputs of the model. In order to simplify our discussion, the major factors are referred to by the notations shown in Table 1.

**Table 1.** Influential parameters for the temperature of the outlet steam.

#### *2.2. Problem Statement*

One of the major control concerns of a reheater is the stability of steamo. In respect to the reheater, some features are reheater-uncontrollable, e.g., smoke temperature and pressure. These features might influence the reheater wall temperature and then change the outlet steam temperature. Steamo has the characteristics of being non-linear and having a large inertia. Due to the change in operation conditions, it may deviate from the expected range. The normal operation changes the smoke flow toward the reheater by adjusting the smoke baffle opening degree. This operation exhibits a long delay before it imposes impacts on temperature. Another method is to spray the desuperheated water to the reheater steam. This method promptly lowers steam temperature, but also reduces the boiler's efficiency. Considering the economic benefits, the first method is always used. The second method is employed only in an emergency, such as when the steam temperature is too high or the working condition is changing.

Similar to the control variables mentioned above, other features also have impacts characterized by different inertias toward the steam temperature. One major concern is the complexity of accurately determining the impact inertia of different features, which highly depends on the physical nature laws of the reheater as well as the operational conditions of the reheater, e.g., combustion stability. One natural choice is to use long delay orders to compose the model inputs. However, the indiscriminate delay order settings make the feature dimension very high and introduce considerable overheads for both storage and computation. Thus, it is important to select the most cost-effective delay order for features while keeping the system model accurate enough.

#### **3. Objective Function for Model Evaluation**

#### *3.1. Multi-Step Prediction*

In order to predict the temperature trend of steamo, the nonlinear autoregressive exogenous model is presented. Differing from other approaches, the proposed model predicts values not for any given time, but for a set of future moments.

Since the reheater system displays different hysteresis characteristics toward different features, modeling the steamo with both short and long hysteresis parameters is important. A multi-step steamo prediction model, which generates a serial of predictions for the next *n* + 1 time steps, is given in Equation (1).

$$\begin{bmatrix} \hat{y}(t) \\ \hat{y}(t+1) \\ \dots \\ \hat{y}(t+n) \end{bmatrix} = \begin{bmatrix} f(\mathbf{x}\_1(t-1), \dots, \mathbf{x}\_1(t-\tau\_1), \dots, \mathbf{x}\_k(t-1), \dots, \mathbf{x}\_k(t-\tau\_k), y(t-1), \dots, y(t-\tau\_y)) \end{bmatrix}, \quad \text{(1)}$$

where *t* is the current time, *t* + *n* is the *n*-th future moment, *xk* is the *k*-th independent variable, *y* is a dependent variable, τ*k* represents the time delay order corresponding to *xk*, and τ*y* is the time delay order of dependent variable *y*.

#### *3.2. Optimization Function*

The prediction target increases the forecast performance for the next *n* + 1 time steps by selecting the most appropriate delay order. However, the total number of delay orders is proportional to the computational complexity and opposite to the model accuracy. Thus, the optimization goal defined is to strike a balance between the computational complexity and modeling accuracy. Accordingly, the objective function is used to minimize the total number of delay orders to minimize the computational complexity. Furthermore, the total number of delay orders is kept as high as possible but within a certain range in order to keep the prediction error low enough. Let ε be the maximum acceptable prediction error for the modeling target; thus, another optimization goal is transferred as one constraint, i.e., the prediction error is smaller than or equivalent to ε. Thus, a constrained optimization problem is formulated as Equation (2).

$$\begin{array}{rcl} \min & f & = & \tau\_{\mathcal{Y}} + \sum\_{k=1}^{K} \tau\_{k} \\ \text{s.t.} & e & = & \frac{1}{m \cdot (n+1)} \|\hat{Y} - \mathcal{Y}\|\_{1} \\ & e & \le & \varepsilon \\ & e\_{l+1} & \le & e\_{l}, \forall l = 1, 2, \cdots, L \\ & 0 & \le & \tau\_{\mathcal{Y}} \\ & \tau\_{k} & \le & \mathbb{C} \end{array} \tag{2}$$

where *K* is the total delay orders of inputs, *m* is the total of test data, *n* is the *n*-th future moment, τ*k* is the delay order of *xk*, and τ*y* is the delay order of the dependent variable. *J* is the total of delay orders. *e* is the error in total *m* samples and *n* + 1 prediction numbers in the form of mean absolute error (MAE). *el* is the error generated by the *l*-th iteration. *C* is the max delay order. is the upper limit of MAE. *Y*ˆ is the prediction value vector and *Y*ˆ = [*y*ˆ(*t*), *y*ˆ(*t* + 1), ... , *y*ˆ(*t* + *n*)]*T*, *Y* is the actual value vector, and *Y* = [*y*(*t*), *y*(*t* + 1), ... , *y*(*t* + *n*)]*T*; *Y*ˆ and *Y* have *m* samples.

#### **4. Delay Order Selection**

In order to accurately select the temporal features, two parts—i.e., the DNN-based prediction model and the GA-based optimal feature selection algorithm—are designed. First of all, the GA generates the individuals of different delay order combinations, which are used as the inputs to the DNN. Then, the DNN outputs the multi-step predictions, which are evaluated by the test sets. The evaluated values are employed as fitness values, which are used in the GA.

#### *4.1. Delay Order Optimization*

Delay order optimization is performed by the GA algorithm. The schema of GA is shown in Figure 2. The algorithm starts from an initial population with 20 individuals and each individual has 28 genes. These randomly generated genes are divided into seven sections. Each section represents an input parameter and has 4 binary numbers which can delay the order range from 0 to 15. Then, the individuals are evaluated by the fitness function, which returns two fitness values (MAE and the total of orders). The different fitness values are assigned different fitness scores. The smaller the MAE value, the higher the fitness scores. In a case in which the MAE values are very close (the difference is below a certain threshold), the smaller the total number of delay orders, the higher the fitness scores. The fitness score determines the probability of being selected as a parent. The probability of being selected is according to the roulette wheel selection, shown in Equation (3).

$$p\_i = \frac{f\_i}{\sum\_{j=1}^{N} f\_j} \tag{3}$$

where *N* is the number of individuals in the population, *fi* is the fitness of individual *i* in the population, and *pi* is the probability of individual *i* being selected in the population.

**Figure 2.** The schematic schema of the genetic algorithm (GA)-based optimized feature selection algorithm. MAE(mean absolute error).

Once the parents are selected, they have a certain probability (*pc*) of being mated randomly and generating new individuals. If the parents are not mated, they become new individuals in the new population. Then, the new population has a certain probability (*pm*) of deciding whether the individual is mutated. Mutating changes (0 changes to 1, or 1 to 0) randomly. The new individuals are evaluated, selected, mated, and mutated until the number of cycles is reached. At the end of the cycle, the GA obtains the best individuals [26,27].

#### *4.2. Prediction Model*

DNN is used to fit the correlation between the future steamo and the historical reheater inlet variables with the accumulated data sets. Figure 3 is the structure of the steamo trend prediction model. Let *m* = τ1 + τ2 + ... + τ*k* be the total of input dimensions to DNN. The outputs of DNN are *n* + 1 values of steamo. DNN has one input layer, two hidden layers, one output layer, and a large number of neurons. The hypothesis function is shown in Equation (4).

$$h(X) = \mathcal{g}(\Theta^3 \cdot \mathcal{g}(\Theta^2 \cdot \mathcal{g}(\Theta^1 \cdot X))),\tag{4}$$

where *X* is a vector with *m* dimensions and Θ1, Θ2, and Θ3 are the weight matrixes between four layers, respectively. *g*(•) is the activation function.

The cost function of DNN is shown in Equation (5).

$$J(\theta) = \frac{1}{2m \cdot n} \sum\_{i=1}^{m} \sum\_{j=1}^{n} \left[ h(X\_j^i) - Y\_j^i \right]^2 + \lambda \cdot L2,\tag{5}$$

where *m* is the total number of samples, *n* is the total number of output variables, *lk* is the number of neurons in the *k*-th layer, and *h*(*Xi j* ) is the prediction value in the *i*-th sample and the *j*-th predict value. *Yi j* is the prediction value in the *i*-th sample and the *j*-th actual value, λ is the regularization parameter, and *L*2 is the regularization term to limit over-fitting. The goal of the DNN is to minimize Equation (5) with the given sets of features and training samples.

**Figure 3.** Multi-step prediction model for steamo. DNN—deep neural network.

#### **5. Experiments and Discussion**

The data for modeling are collected every 3 s from unit 3 and unit 4 by the distributed control system (DCS). Unit 3 and unit 4 are two ultra-super-critical 1000 MW power plants with the same structure. In our experiment, in total, 7,084,800 records are used for evaluation, in which unit 3 and unit 4, respectively, have 3,542,400 records from 1 May 2016 to 31 August 2016.

#### *5.1. Data Preprocessing*

In the data preprocessing process, two steps are taken: Outlier removal and standardization.

Outlier removal: The outliers that violate the physical or technical limitations might affect the model's performance and should be removed before modeling. (1) The points out of the normal range of physical or technical are replaced with the average of adjacent points. For instance, for a certain period, the temperature of steamo should be around 600 ◦C; thus, the points below 594 ◦C that violate the steady change characteristics of temperature should be replaced. (2) The errors of Dwater control time should be modified. Under normal circumstances, the Dwater control time (more than 0) takes a few minutes. For instance, if the collected data shows that the control time lasts for several hours, the abnormal control time will be modified to a maximum of 3 min.

Standardization: The different features might have different range of values. If these variables are used directly, the feature data with small values may be ignored, while the ones with large dimensions will be selected. Therefore, the Z-score standardization technique [28] is used to scale the data to the ones with a mean value of 0 and a standard deviation of 1, which will speed up the iteration rate of the optimization and convergence.

#### *5.2. Experiment Settings*

The parameters of DNN and GA are shown in Table 2. The DNN is a 2-hidden-layer neural network, and the learning rate is set to 0.001. MAE, which is the average absolute differences between predictions and actual observations, is used to evaluate the modeling error. Tanh is chosen as the activation function since it achieves the smallest average MAE compared to other activation functions (e.g., identity, logistic, relu) for the chosen data set.

The 4-month data for unit 3 and unit 4 are divided into 20 different sets. Each set consists of training data from 7 days (about 201,600 records) and test data from 1 day (about 28,800 records).

**Table 2.** The parameters of DNN and GA.

#### *5.3. Results and Discussion*

This proposed method is evaluated from three different perspectives: Firstly, a one-round simulation is performed with a set of data to demonstrate its capability for finding the optimal delay order for different features; secondly, the experiment is implemented on unit 3 and unit 4 at different times to demonstrate the adaptability of the presented method; finally, the delay order identified with data from the unit 3 is directly used in the modeling process for unit 4 to check its capability for generalization.

#### (1) Results of the one-round simulation

As for getting the preliminary delay order in unit 3, the data from ~23 July 2016–30 July 2016 is selected as the experiment data. The changes of MAE and the total number of selected orders during the iteration process are shown in Figure 4a. The accuracy level of MAE is set as 0.001. In the early iterations, MAE begins to decrease while the total delay order increases. Then, until MAE stabilizes at 0.13—i.e., the lower limit of MAE—the total delay order decreases. In the later iterations, these criteria remain constant, which indicates that the algorithm is converged. Figure 4b shows each feature's delay order. It can be seen that some features have a larger delay order, e.g., smokep, which indicates large hysteresis, while in contrast, the order of Dwater shows timely but transient impacts.

(**a**) Fitness curve during the experiment (**b**) Optimal delay order

**Figure 4.** Results for the one-round simulation for unit 3 (~23 July 2016–30 July 2016).

In Figure 5, the forecasting errors in one-minute periods with 20 points in 30 July 2016 are plotted in a box plot which displays the distribution of five different metrics, i.e., minimum, first quartile, median, third quartile, and maximum. Figure 5 shows that MAE increases with the increase in the predicting time step. This is normal, as timely response factors, such as steamp, smoket , and baffleo, cannot be captured by predictor. However, the median MAE in one minute is less than 0.3 ◦C, and the average is near 0.1 ◦C. According to Figure 4b, the maximum delay order of the reheater steam temperature steamo is 13. This means that the historical data of steamo have major impacts on the

accuracy of the model. It also shows that, in the current system, steamo is not well controlled, as it should kept steady around 600 ◦C.

**Figure 5.** The box error curve.

#### (2) Comparisons of unit 3 and unit 4 from different perspectives

The feature selection method is tested for both unit 3 and unit 4 based on the operational data from 1 May 2016 to 31 August 2016. Since the records from some days contain too many abnormal data, the data from those days are not used for the model training. As shown in Table 3, the data periods are closed from the intra-comparisons within unit 3 or unit 4 or the inter-comparison between those two units.

Table 3 shows that the general range of the seven studied features has the corresponding length of delay orders with respect to their inertia toward steamo. For all 20 tests, there is no significant deviation regarding MAE. This means that the designed DNN with the selected features as the inputs achieves good convergence. It also shows that the delay orders of smoket and smokep are larger than those of steamt and steamp, as the smoke has indirect impacts toward the steamo. Thus, their delay orders are much larger than those of the feature of the inlet steam. Dwater has a very small delay order due to the fast temporal response toward steamo. For certain periods, the delay orders of Dwater are zero, e.g., in tests 9, 10, 16, and 18. The zero value is due to the lack of training data for Dwater. In those periods, the action of spraying de-superheated water is seldom performed. This is due to the insufficient training samples. At these stages, the numbers of sprays are, respectively, 31, 22, 26, and 18, while other tests have about 60 actions, owing to the comparable steam*o* which is more stable. A similar phenomenon can also be observed for the optimal delay order for baffleo. These results show the importance of the data coverage for the accuracy of feature selection.

**Table 3.** Results for both unit 3 and 4 (value before "/" is for unit 3 and after is for unit 4). MAE—Mean absolute error.

#### (3) Determination of delay order

For the purpose of controlling steamo changes within the ideal range, properly finding a delay order is crucial to accurately describing the hysteresis of features for a prediction model. The variations of delay orders for each feature are shown in Figure 6; the shadow ranges from the maximum to minimum delay order. There is a large overlap between two units, which indicates the existence of common delay orders. The medians of overlap (2, 6, 10, 10, 2, 1, and 14) represent the general level of intervals and may serve as the references for delay orders regarding the steamo system of ultra-super-critical 1000 MW power plants.

**Figure 6.** Delay order distribution of the seven features.

The features with delay orders of 2, 6, 10, 10, 2, 1, and 14 generated from the data from unit 3 are used as selected features for the reheater steam temperature prediction. We also adopt the same methods to find the optimal feature distributed for the unit 4. Then, those results are compared with the dataset of test 1 to test 20, which are from unit 4. The orange bars indicate the MAE with the identified delay order. The directly calculated optimal solution is shown by the blue bars. Figure 7 shows the comparisons, which obviously indicate that the MAEs of two cases are approximately equal. The maximum error is only 0.9% (on the 16th day), which means that it is almost the same as the results from the optimal solutions. This shows that the selected delay orders (2, 6, 10, 10, 2, 1, and 14) have good generalization capability, and can, it is argued, represent the physical characteristics of two reheaters.

**Figure 7.** Comparisons between optimal and proposed methods. Blue bars are the MAE of the optimal solution and the orange bars indicate the MAE of the proposed method.

#### **6. Conclusions**

For many industrial processes, it is important to find the best feature delay orders as well as features that are most correlated with the prediction targets. In this paper, a delay order identification method based on GA and DNN is proposed. This method adopts the GA to generate candidate feature sets which try to find minimal numbers of features while keeping the MAE of the prediction model low enough. The DNN model is used for modeling processes that generate the multi-step predictions typically demanded in many industrial processes. This method is evaluated with experiments from different perspectives; data from two similar units are used to check whether the found time delays indeed demonstrate the physical characteristics of the underlying systems. The experimental results indicate that two units have similar delay orders and the delay order can be directly used for modeling similar devices with little loss of accuracy.

Of course, many interesting issues still need to be investigated. For instance, our solution limits the temporal feature selection. It is important for the delay order selection method to support both spatial and temporal feature selection. We are investigating the use of an attention mechanism to find the optimal solution for both dimensions. In addition, the GA demands considerable resources and computational costs. We are working to design more computationally efficient methods, e.g., filter-based feature selection for industrial feature processing.

**Author Contributions:** N.G. proposed the main idea of the method; J.L. and Z.Q. implemented the model and validated the field test. W.G. provided the funding.

**Funding:** This work is funded by the Nature Science Foundation of China 61403429, 61621062 and 61772473.

**Acknowledgments:** This work is funded by the Nature Science Foundation of China 61403429, 61621062 and 61772473.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**MECHANICAL ENGINEERING**

http://dx.doi.org/10.5772/2397 Edited by Murat Gokcek

#### **Contributors**

Lena Zentner, Valter Boehm, Horng, Ming-Pin Lai, Daisuke Yoshino, Masaaki Sato, Cuneyt Fetvaci, Constance Ziemian, Mala Sharma, Sophia Ziemian, Emily Hunt, Pam Lockwood, Michelle Pantoya, Diana V. Bambill, Igor Pšunder, Isad Saric, Dragan Mladen Cvetković, Yangjie Wei, Chengdong Wu, Zaili Dong, Sorin Ioan Borza, Paul Dan Brindasu, Livia Beju, Olivera Popović, Radica Prokic-Cvetkovic, Fabrício Augusto Matheus Moura, José Roberto Camacho, Geraldo Caixeta Guimarães, Marcelo Lynce Ribeiro Chaves, Nebojsa Mitrovic, Milutin Petronijevic, Vojkan Kostic, Borislav Jeftenic, Caslav Mitrovic, Aleksandar Bengin, Nebojsa Petrovic, Jovan Jankovic, Hermes Giberti, Simone Cinquemani, Giovanni Legnani, Hong Yan, Philippe Velex, Carsten Schmidt, Stefan Cuber, Adriano Fagali De Souza, Sabrina Bodziak, Abraham Segade-Robleda, José Antonio Vilán Vilán, Marcos López Lago, Enrique Casarejos Ruiz, Tomasz Galka, Marcelo Rubén Pagnola, Rodrigo Ezequiel Katabian, Hasril Hasini, Hiroaki Endo, Nader Sawalhi, Sheng-Hsueh Yang, Shenq-Yuh Jaw, Keh-Chia Yeh, Kazuhiko Ogawa

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**5. Post-operative hernia**

mesh tacking in the laparoscopic approach, which can be side stepped by avoiding tacking in known areas of nerves distribution [1]. O'Reilly et al. [29] found that the risk for post-herniorrhaphy neuralgia and/or numbness was significantly lower with laparoscopic approach when

• Anterior thigh area

• Mons pubis and Labia majora

• Skin of the gluteal area

• Anterior lateral thigh area

• The root of the penis and upper scrotum

The first line in the management of neuralgia is usually conservative, mainly by local anesthesia injections in the affected groin. When this modality fails, surgical re-exploration is advocated to identify the affected nerve and excise it. On rare cases of patients presenting with pain not matching the distribution of a single nerve, surgical re-exploration is not advised as it usually will fail improving the pain and may result in damaging more structures [15, 38].

Bladder, testicular, and vas deferens injuries are among the commonly injured visceral organs with groin herniorrhaphy procedures Among the least injured structures are the ureters which are more often seen with the laparoscopic approach- the most common type of injury is incomplete transection of the ureter and ureteral perforation [39–41]. Bladder injuries are frequently reported with direct inguinal hernias, and in rare cases could result in a sliding hernia, in which part of the bladder adheres to the hernia sac. Thus, direct sacs are usually

Testicular swelling and atrophy could develop after inguinal hernia repair. Swelling and edema of the scrotum are due to hematoma or edema of the inguinal canal that progress inferiorly to the scrotum with gravity. On one hand, testicular atrophy is associated with blood supply injury during the process of dissection and isolation of the cord and usually is a painless complication. On the other hand, testicular pain post-operatively could be a result of torsion or abscess and ruling out such suspicion is done by ultrasound imaging. In the pediatric

inverted back into the peritoneal cavity to avoid unnecessary dissection [42, 43].

compared with open approach [28].

**Table 2.** Commonly injured nerves post-herniorrhaphy [1].

 Visceral injury**

Also known as an incisional hernia, post-operative hernias occur as a direct result of fascial tissue failure to heal post laparotomy. Although incisional hernias are frequently seen either post mid line and/or transverse incisions, it can, in theory, happen after any surgical incisions like paramedian and McBurney incisions, and are also seen post laparoscopic surgeries [50]. Such hernias can grow to huge sizes and contain a significant amount of small and large bowel. Previously, the incidence was believed to be around 20%, but recent epidemiological studies estimate the number to be 11%. Around two-thirds of cases may present within the first 12 months after the operation, while the other -third present as a late complication after 5–10 years [51–53]. Risk factors of incisional hernias are increased with advanced patient age, malnutrition, immune-compromised state, smoking, and obesity [12, 15, 54, 55]. Other factors that play an important role include emergency surgery and post-operative wound infection. One major complication of incisional hernia repair surgery is a high recurrence rate, which might reach up to 50%. In some cases this risk is related to the type of surgical approach, whether suture repair or mesh supported repair, and also to the amount of tension applied on the wound edges. Recurrence in this type of hernia is also related to the appearance of unrecognized hernia sites [56]. Another set of complications is related to the empty hernia cavity that is left behind post reduction of hernia sac, such as hematomas and seromas. Henceforth, experts recommend placement of closed suction drainage; which by itself along with mesh will increase the risk of infection post-operatively [57, 58].

before surgery, and fluids should be managed judiciously along with continuous monitoring of the patient vital status [65–67]. While post-operative period is mainly concerned with pain management, fluid and diet, avoidance of nasogastric tube and early urinary catheter removal,

Worst Case Scenarios! Complications Related to Hernial Disease

http://dx.doi.org/10.5772/intechopen.76079

101

An umbilical hernia is usually seen in the pediatric population with an incidence of 10–30% at birth in infants of Caucasian ethnicity and higher in those of African-American ethnicity, for unknown reasons [1]. It is also more common in premature infants of all races, and some report a tendency for familial inheritance. While the cause is yet to be identified in most of the cases, an umbilical hernia usually will regress and close on its own by 2–3 years of age with

Meanwhile, umbilical hernias in adults have a different clinical presentation, most being acquired not congenital with a male to female ratio of 3:1. The adult-type umbilical hernia usually will need surgical intervention for it to close and usually are symptomatic at time of presentation. A typical presentation will be of an exquisitely tender peri-umbilical mass overlying the skin; long-standing untreated umbilical hernia might result in thinning of covering skin and ulceration due to pressure necrosis of the adjacent skin. While small umbilical hernias could pass unnoticed and discovered incidentally. This type of hernia is associated usually with recurrence in the setting of high intra-abdominal pressure. For this reason, surgical repair is offered for incarcerated hernia or a progressively symptomatic

Although the overall incidence of inguinal hernia in the pediatric population is low when compared with adults, the complication that might arise is almost the same. In the age group, bowel incarceration is incidence is low, but should this be the case, bowel infarction would happen within 2–3 hours. With bowel infarction, it is not uncommon to get testicular blood supply compromise leading to ischemic necrosis and testicular atrophy with an incidence around 9% according to some studies [71–73]. While in girls, ovarian torsion is reported to happen with inguinal hernia strangulation in about third of patients with incarcerated hernia that contain an irreducible ovary. For this reason, some experts recommend not to delay sur-

The congenital diaphragmatic hernia is caused by a diaphragmatic defect resulting abdominal viscera herniating to the chest. It usually presents in the first few hours of life with respiratory distress so severe that it could be incompatible with life [75]. In many cases, this condition

early mobilization, and finally early discharge [68, 69].

**7. Hernia and the pediatric population**

less than 10% needing surgical intervention.

gical intervention in this population [74].

**7.3. Congenital diaphragmatic hernia**

**7.1. Umbilical hernia**

type [3, 11, 70].

**7.2. Inguinal hernias**

### **6. Enhanced recovery after hernia surgery**

The aim of enhanced recovery after surgery protocols is to improve outcomes, lower health cost, while harnessing the benefits by standardizing the medical care [59, 60]. Such protocols are evidence-based guidelines that include minimizing surgical trauma, post-operative pain, reduce complications, and improve outcomes by decreasing the expected length of hospital stay and fasten the patient recovery [61]. Such approach to patient care should be a multidisciplinary approach including surgeon, anesthesiologists or pain specialists, nursing staff, physical rehabilitation service, and most importantly patient cooperation [62, 63]. Patients who are followed with an enhanced recovery protocol will have the same discharge criteria but will reach these milestones sooner. This approach will usually contain 15–20 elements and will span through the full patient hospital stay; preoperatively, intra-operatively, and postoperatively (**Table 3**) [64]. Before surgery, patient education and counseling about current treatment options and best approach should be discussed. After that, a meticulous overview of the patient general health condition and management of any comorbidities such as renal, cardiac, or respiratory should be done. Intra-operatively prophylactic antibiotics are recommended

**Table 3.** Main criteria for enhanced recovery after surgery protocol [66].

before surgery, and fluids should be managed judiciously along with continuous monitoring of the patient vital status [65–67]. While post-operative period is mainly concerned with pain management, fluid and diet, avoidance of nasogastric tube and early urinary catheter removal, early mobilization, and finally early discharge [68, 69].


*2.4. Selecting the Optimal Wavelength*

One HSI dataset contained the spectral information of samples from 400 nm to 1000 nm, simultaneously. However, certain wavelengths had redundant data, resulting in the timeconsuming processing of HSI data [28]. Therefore, it is necessary to eliminate wavelengths containing redundant and irrelevant information to optimize the texture profiles for data analysis samples using the wavelength/variant selection of hyperspectral data [23]. Regarding wavelength selection for HSI analysis, the regression coefficient (RC) is commonly utilized [29,30]. We utilized the RC to determine the optimal wavelength that contributed the most to the prediction of TPA values in common carp muscle. In the calculation of RC, the optimal wavelength is chosen by computing the β-coefficient from the full-wavelength PLSR model. The wavelength with the highest absolute value of the β-coefficient is considered to be the optimal wavelength [30]. The program for RC was operated in MATLAB 2021a software (The MathWorks Inc., Natick, MA, USA).

#### *2.5. Measuring the Texture Indicators of Common Carp Muscle*

We extracted the muscle corresponding to each of the four skin regions. The muscle size was 20 mm × 20 mm × 15 mm. Eight texture indicators of the muscle, including gumminess, springiness, cohesiveness, resilience, hardness, brittleness, adhesiveness, and chewiness, were measured using a texture analyzer (TA.XTC-18, Baosheng, Shanghai, China) and a TA/36 cylindrical probe. The measurement speed was 2 mm/s, and the

trigger force was 5 N. The compressive deformation of one sample was set to 60%. These eight texture indicators were derived from the TPA curves of each sample, and the TPA parameters listed above were calculated using Bourne's technique [31].

For each texture indicator, to examine whether there were significant differences among four muscle regions, we measured the distances with PCA analysis using Tassel 5.0 [32]. The Spearman correlation coefficient of the contents of any two indicators in four samples was calculated using the R 'cor. test' function in the R software (version 4.0.2).


*Article* **Investigation on Energetic Efficiency of Reactor Systems for Oxidation of Micro-Pollutants by Immobilized Active Titanium Dioxide Photocatalysis**

**Simon Mehling 1,\* , Tobias Schnabel 2 and Jörg Londong 1**

**Abstract:** In this work, the degradation performance for the photocatalytic oxidation of eight micropollutants (amisulpride, benzotriazole, candesartan, carbamazepine, diclofenac, gabapentin, methlybenzotriazole, and metoprolol) within real secondary effluent was investigated using three different reactor designs. For all reactor types, the influence of irradiation power on its reaction rate and energetic efficiency was investigated. Flat cell and batch reactor showed almost similar substance specific degradation behavior. Within the immersion rotary body reactor, benzotriazole and methylbenzotriazole showed a significantly lower degradation affinity. The flat cell reactor achieved the highest mean degradation rate, with half time values ranging from 5 to 64 min with a mean of 18 min, due to its high catalysts surface to hydraulic volume ratio. The EE/O values were calculated for all micro-pollutants as well as the mean degradation rate constant of each experimental step. The lowest substance specific energy per order (EE/O) values of 5 kWh/m3 were measured for benzotriazole within the batch reactor. The batch reactor also reached the lowest mean values (11.8–15.9 kWh/m3 ) followed by the flat cell reactor (21.0–37.0 kWh/m3 ) and immersion rotary body reactor (23.9–41.0 kWh/m3 ). Catalyst arrangement and irradiation power were identified as major influences on the energetic performance of the reactors. Low radiation intensities as well as the use of submerged catalyst arrangement allowed a reduction in energy demand by a factor of 3–4. A treatment according to existing treatment goals of wastewater treatment plants (80% total degradation) was achieved using the batch reactor with a calculated energy demand of 7000 Wh/m3 .

**Keywords:** photocatalysis; micro-pollutant treatment; titanium dioxide; reactor design; energy per order; immobilized catalyst

### **1. Introduction**

Within our industrialized society, thousands of chemical compounds are used daily for industrial and household purposes. Within the European Union, for example, over 100 thousand unique substances are registered within the European Community (EC) inventory [1]. Some of these substances, such as pharmaceuticals, industrial chemicals, and pesticides, enter the wastewater stream and can be discharged into downstream aquatic systems due to inadequate treatment performance of conventional wastewater treatment technologies [2,3]. Despite their low concentrations with a range of ng/L to µg/L, these micro-pollutants represent a challenge for future water and drinking water protection [4]. An additional treatment stage for municipal wastewater is necessary as an important barrier to the emission of these pollutants. This (4th) treatment stage is usually arranged after the biological treatment stage of wastewater treatment plants. The state of the art of treatment mostly consists of adsorptive processes, which use granulated or powdered activated carbon, and oxidative processes, which use ozone [5–7]. The cumulative energy

**Citation:** Mehling, S.; Schnabel, T.; Londong, J. Investigation on Energetic Efficiency of Reactor Systems for Oxidation of Micro-Pollutants by Immobilized Active Titanium Dioxide Photocatalysis. *Water* **2022**, *14*, 2681. https://doi.org/10.3390/w14172681

Academic Editors: Huijiao Wang, Dionysios (Dion) Demetriou Dionysiou and Yujue Wang

Received: 12 July 2022 Accepted: 25 August 2022 Published: 29 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

demand of a large-scale ozonation can be assumed to be 50–100 Wh/m3 [8]. For this application, a biological post treatment, in most case an aerated sand filtration, is necessary due to transformation products, which requires a cumulative energy consumption of 20–50 Wh/m3 [8]. Powdered activated carbon (PAC) applications also require the use of sand filtration, which can be associated with an energy demand of 120 Wh/m3 . Further relevant energy consumption occurs during the production of activated carbon and can be quantified as 350 Wh/m3 (primary energy/treated wastewater) [8]. These values can be seen as a first point of comparison towards the state of the art.

Advanced oxidation processes (AOP), which use cold oxidation by hydroxyl radicals as a core mechanism, are not yet used on a broad industrial scale within municipal wastewater treatment [9]. The main advantage of these applications is the higher oxidation potential of the hydroxyl radicals, which in principle allows a complete mineralization of treated pollutants to *CO*2, water, and mineral salts [9,10]. During photocatalysis on titanium dioxide, irradiation of the catalyst with UV-A light (320–400 nm) leads to charge separation and, thus, to the formation of electron holes and free electrons [11]. The use of this process is possible in several variations. Existing developments can be differentiated in terms of the light source (wavelength, natural/artificial, type), the catalyst (*TiO*2-based/other material, immobilized/suspended, size), and the reactor design and operation [12]. The emphasis of this work is the use of immobilized *TiO*2 and UVA-LEDs as light source for the treatment of micro-pollutants in wastewater. A comparison of reactor systems is possible by a number of performance indicators [12,13]. In the context of economic competitiveness, the energy efficiency is an important parameter if artificial irradiation is used Wang et al. 2021 [14]. The electrical energy required to degrade a pollutant by a factor of 10 in relation to the volume of water treated is called electrical energy per order (EE/O) and is usually given as a characteristic value [14]. Within the literature, values from 5 ∗ 104 up to 10−1 kWh/m3 are reported for different pollutants and reactor systems [14]. Most of these systems described in [12] are in the range of 10–50 kWh/m3 . In addition to the pollutant treated and the type of reactor, degradation rates and, thus, energy efficiencies get influenced/decreased by a variety of other parameters, especially matrix influences [15,16]. A realistic assessment of the suitability of photocatalytic processes for wastewater treatment consequently requires an investigation of a wide range of micro-pollutants in real matrix. [17] achieved EE/O values of 0.21 kWh/m3 for diclofenac and 2.1 kWh/m3 for carbamazepine using suspended *TiO2* and UVC-lamps when treating river water with a photocatalytic membrane reactor module. In contrast, [18] stated, that no strictly photocatalytic experiment measured EE/O values lower than 10, except the experiments of [19], which was conducted using ultrapure water at pH 3.8. [20] measured EE/O values of 15–57.3 kWh/m3 for the treatment of azithromycin, trimethopirm, oflaxacin, and sulfamethoxazole within secondary effluent via suspended *TiO2* photocatalysis.

The content of this work is the comparison of three reactor types for the photocatalytic degradation of eight micro-pollutants based on experimental measurement data within secondary effluent. The catalyst used was immobilized *TiO2*, which is suitable for wastewater treatment due to its low resource cost and toxicity, if nanoparticulate emission can be avoided. The focus lays on the energetic efficiency of these applications, since this parameter has a significant influence on the economic competitiveness compared to conventional processes of the 4th treatment stage. The energetic comparison is carried out in this work using the EE/O methodology. It must be noted that, at least for the German context, a degradation of 90% is substantially beyond currently discussed cleaning targets. Existing requirements for the degradation performance of these applications require an 80% elimination of a selection of indicator substances relative to the raw wastewater [21,22]. A detailed comparison with conventional treatment technologies requires a detailed configuration of a photocatalytic treatment stage considering variable degradation performances of upstream biological stages and, thus, exceeds the scope of this work. As a rough estimate, it can be assumed that a 20% average micro-pollutant degradation is achieved within the biological stage of a wastewater treatment plant [2,8]. Assuming 75% elimination within a

(photocatalytic) 4th treatment stage (relative to secondary effluent), this results in a total degradation of 80%. Assuming pseudo 1st order reaction kinetics, which applies for the photocatalytic treatment of most micropollutants, a non-linear reduction of the energy demand of about 40% for a reduced elimination rate (90% to 75%) is to be expected.

### **2. Methods**

### *2.1. Chemicals and Materials*

A more detailed description of the catalyst material used is given in previous works [23,24]. It consists of a suspension of anatase-modified titanium dioxide nanoparticles of 14 nm diameter and an anatase content of the titanium dioxide of 90%, which is coated onto a stainless-steel mesh. The V4A steel mesh had a mesh size of 200 µm and a wire thickness of 100 µm. A 10 µg/L terbutryn (Sigma Aldrich, St. Louis, MO, USA) solution in methanol was used within analysis as an internal standard. Further analytical purity chemicals were used for calibration: amisulpride (Gentham Life Science, Corsham, UK), benzotriazole (Gentham Life Science), candesartan (BLD Pharma, Hyderabad, India), carbamazepine (Sigma Aldrich), diclofenac (Cayman Chemical Company, Ann Arbor, MI, USA), gabapentin (Gentham Life Science), 1-methylbenzotriazole (Chempure, Bengaluru, India), and metoprolol (Sigma Aldrich).

### *2.2. Experimental Set-Up and Procedure*

The investigations were carried out in different time series in 2019, 2020, 2021, and 2022. The investigated wastewater was always taken during dry weather conditions from the effluent of the wastewater treatment plant Weimar-Tiefurt and were filtered (50 µm) before use. The treatment plant has a capacity of 100,000 inhabitants. It consists of a mechanical treatment stage and a double channel cascade biological treatment system with horizontal secondary sedimentation. In total, three different reactor systems were used:

Data regarding the individual cleaning performance are already published for the flat cell [24] and the immersion rotary body reactor [23].

As shown in Figure 1, the laboratory batch reactor consists of an aluminum cylinder with a diameter of 14 cm and a hydraulic volume of 500 mL. The irradiated catalyst area is 0.0154 m2 . The reactor is stirred continuously at 300 rpm. Six LEDs with a total electrical power of 12 W are installed and the power can be manually controlled. The catalyst is placed horizontally, submerged in the reactor 5 mm below the water surface. The reactor was investigated in batch mode. Before starting the experiment, the reactor was flushed with wastewater. Afterwards, a 30-min concentration equilibration was performed whereby the reactor was mixed and not irradiated. Subsequently, a photocatalytic treatment was carried out for 90 min, with 5 mL of sample material being taken every 15 min.

The flat cell reactor studied, as shown in Figure 2, consists of a pleated steel mesh coated with *TiO*2 and eight UV-A LEDs (365 nm). The catalyst is placed inside a stainlesssteel enclosure with glass windows on both sides. The edge length is 11.5 cm, and the hydraulic volume is 200 mL. The pleated design of the catalyst allowed a surface area of 0.0181 m2 to be arranged on a base area of 0.0128 m2 . On each side of the reactor, 4 UV-A LEDs (1.8 W each) were evenly spaced at 8 cm distance. The reactor was constantly fed via peristaltic pump. Within the experiments, a hydraulic residence time of 12.17 min was set. After a run time of 30 min, which ensured a stationary reactor condition, the inlet and outlet of the reactor were sampled.

4 of 15

**Figure 1.** Schematic 3D-Model of the photocatalytic batch reactor. **Figure 1.** Schematic 3D-Model of the photocatalytic batch reactor. outlet of the reactor were sampled.

*Water* **2022**, *14*, x FOR PEER REVIEW 4 of 15

**Figure 2.** Schematic 3D-Model of the photocatalytic flat cell reactor. **Figure 2.** Schematic 3D-Model of the photocatalytic flat cell reactor.

**Figure 2.** Schematic 3D-Model of the photocatalytic flat cell reactor. The photocatalytic immersion body reactor in Figure 3 consists of 12 rotating discs of 36 cm diameter on which the catalyst material is placed on both sides. Total catalyst area is 2.44 m², of which about 50% is irradiated via UV-A LED arrays. The hydraulic part of The photocatalytic immersion body reactor in Figure 3 consists of 12 rotating discs of 36 cm diameter on which the catalyst material is placed on both sides. Total catalyst area is 2.44 m2 , of which about 50% is irradiated via UV-A LED arrays. The hydraulic part of the reactor is divided into 12 cascades, which are flown through in series with a total volume of 25 L. Investigation and sampling were carried out in the following steps: replace reactor volume with fresh wastewater, start reactor feed and operation and let run for 90 min, stop the reactor feed and operation, and sample. For this reactor, one sample is taken from every second of the twelve cascades. In contrast to previous laboratory applications, continuous feeding continues to take place directly from the effluent of the Weimar-Tiefurt wastewater

The photocatalytic immersion body reactor in Figure 3 consists of 12 rotating discs of 36 cm diameter on which the catalyst material is placed on both sides. Total catalyst area is 2.44 m², of which about 50% is irradiated via UV-A LED arrays. The hydraulic part of

treatment plant, whereas in the laboratory scale sample, the material was fed to the reactors via a tank, which was taken as a quick sample (about 20 L). furt wastewater treatment plant, whereas in the laboratory scale sample, the material was fed to the reactors via a tank, which was taken as a quick sample (about 20 L).

the reactor is divided into 12 cascades, which are flown through in series with a total volume of 25 L. Investigation and sampling were carried out in the following steps: replace reactor volume with fresh wastewater, start reactor feed and operation and let run for 90 min, stop the reactor feed and operation, and sample. For this reactor, one sample is taken from every second of the twelve cascades. In contrast to previous laboratory applications, continuous feeding continues to take place directly from the effluent of the Weimar-Tie-

*Water* **2022**, *14*, x FOR PEER REVIEW 5 of 15

**Figure 3.** Schematic 3D-Model of the photocatalytic immersion body reactor (© Lynatox GmbH, 2020). **Figure 3.** Schematic 3D-Model of the photocatalytic immersion body reactor (© Lynatox GmbH, 2020).

The experimental program is divided according to the reactor type. For the batch reactor, the degradation of micro-pollutants was investigated under the influence of the electrical irradiation power at constant treatment time. For the other continuous flow reactors, a variation of the irradiation power at a constant feed rate was also carried out. An overview of the investigated parameters can be seen in Table 1. The investigated radiation power per reactor volume was varied for different reactor systems. There are overlapping ranges for all reactor systems with a total magnitude of about 3.08–57.60 kW/m³. The LED power relative to the irradiated catalyst area was also similar for the batch and immersion The experimental program is divided according to the reactor type. For the batch reactor, the degradation of micro-pollutants was investigated under the influence of the electrical irradiation power at constant treatment time. For the other continuous flow reactors, a variation of the irradiation power at a constant feed rate was also carried out. An overview of the investigated parameters can be seen in Table 1. The investigated radiation power per reactor volume was varied for different reactor systems. There are overlapping ranges for all reactor systems with a total magnitude of about 3.08–57.60 kW/m3 . The LED power relative to the irradiated catalyst area was also similar for the batch and immersion rotary body reactor (100–300 W/m2 ). In the experiment with the flat-cell reactor, higher LED powers per catalyst area of up to 636 W/m2 were additionally investigated.

rotary body reactor (100–300 W/m²). In the experiment with the flat-cell reactor, higher LED powers per catalyst area of up to 636 W/m² were additionally investigated **Table 1.** Experimental reactor operation and geometric parameters. **Batch Reactor**  The values of the catalyst area-related irradiation power must be understood as a coefficient for reactor configuration and do not represent the actual average irradiation intensity on the respective catalysts. Due to the emission angle of the LEDs being 120◦ and the LED gap being 8 cm for the flat-cell reactor, considerable lateral losses are to be expected here. Furthermore, adsorption and reflection dynamics or other geometric characteristics of irradiation sources are not considered within this value.

> Irradiated catalyst area per hydraulic volume [m²/m³]

> > 30.79

1.2 199.88 6.15 90 1.3 299.82 9.23 90

**Immersion Rotary Body Reactor**

LED-power per hydraulic volume [kW/m³]

Treatment time [min]

3.08 90

LED-power per irradiated catalyst area [W/m²]

1.1 99.94

Number

**Table 1.** Experimental reactor operation and geometric parameters.

### *2.3. Analytical Methods*

The micro-pollutant analysis for the flat-cell and immersion rotary body reactor experiments was conducted using LC-MS/MS analogous to previous publications [24]. The HPLC was a Dionex R3000 system (ThermoFischer, Waltham, MA, USA) with gradient pump and autosampler. The HPLC eluent was ultrapure water with 1 mmol/l ammonium acetate and acetonitrile with 0.1% acetic acid. A Synergy 2.5 µm hydro PP column (Phenomenex, Aschaffenburg, Germany) measuring 100 2 mm was used as analytical separation column. The flow was 0.25 mL/min, and the gradient was run from 4% acetonitrile to 96% acetonitrile in 28 min. The injection volume was 100 µL sample, which was mixed with 10 µL internal standard. An API 4000 triple quadrupole mass spectrometer (Sciex, Framingham, MA, USA) with electro spray ionization was used for detection. For each substance, two multiple reaction monitoring mass transitions were evaluated. The micro-pollutant measurements of the batch reactor experiments were carried out by the commercial laboratory "GWA Umweltanalytik Luisenthal" following [25]. The following substances were analyzed: amisulpride, benzotriazole, candesartan, carbamazepine, diclofenac, gabapentin, 1-methylbenzotriazole, and metoprolol. All analytical measurements were carried out at least in duplicate.

### *2.4. Kinetic and Modelling*

The kinetics of heterogeneous photocatalytic degradation can be described by the Langmuir–Hinshelwood (L–H) model [26,27], where the reaction rate of the parent substances can be calculated as follows:

$$r = -\frac{d\mathcal{C}}{dt} = k\_{LH}\frac{K\_L\mathcal{C}\_p}{1 + K\_L\mathcal{C}\_p + \sum\_{i=1}^n K\_i\mathcal{C}\_i(i=1,n)}\tag{1}$$

The reaction rate *r* is determined by the specific reaction constant *kLH*, the Langmuir constant *KL*, and the equilibrium concentration *Cp*. The constant *kLH* is significantly influenced by the formation and recombination rate of radicals. These are again significantly influenced by the activity of the catalyst, the radiation power, and the concentration of electron acceptors in the liquid matrix. The constant *KL* describes the substance-specific adsorption capacity of the reactant on the catalyst material. *Ki* and *Ci* are the Langmuir constant and the concentration of every substance within the solution. However, the application of this model in practice is very difficult, because, on the one hand, in a wastewater matrix in the rarest cases, all ingredients can be determined and the adsorption of the investigated micro-pollutants on the catalyst within the present concentration ranges cannot be measured. However, according to the observations of many authors, the practically observed reaction follows the first order [26]. Thus, the following simplification is assumed:

$$r = -\frac{d\mathbb{C}}{dt} = k\_{LH} \mathbb{K}\_L \mathbb{C}\_p = k \mathbb{C}\_p \tag{2}$$

For easier readability of measured reaction rates, they have been presented within this work as half-lives according to Formula (3):

$$T\_{1/2} = \frac{\ln 2}{k} \tag{3}$$

In order to compare the efficiency of different reactor systems, it is necessary to balance the amount of energy used, in this case electrical irradiation power, per unit volume of treated wastewater. The volume-specific energy requirement is calculated according to the following formula:

$$E\_{spez} = \frac{P\_A \* A\_V \* V\_R}{Q} \tag{4}$$

Here, *PA* is the electrical radiation power per catalyst surface, *AV* is the catalyst area used per volume, *VR* is the reactor volume, and *Q* is the volumetric flow rate. Assuming a 90% degradation rate, the feed rate can be calculated from reactor volume and reaction rate on a substance-specific basis. Under this assumption, Formula (5) can be represented as follows:

$$EE/O\_{\bar{i}} = P\_A \ast A\_V \ast T\_{\bar{i}} \tag{5}$$

where, *Ti* is the treatment or hydraulic retention time to achieve a given degradation ratio of a micropollutant within a specific experimental set-up. By means of Formula (6), an energy balance for a batch reactor is also possible. For flow reactors this balancing is only valid if a plug-flow behavior can be assumed. This proof as well as the corresponding 1st order reaction rate constants have been provided experimentally for the flat cell reactors within [24]. The 1st order reaction rate constants for immersion rotary body were determined in [25]. The time *Ti* was calculated using Formula (6):

$$T\_i = \frac{\ln(1 - \eta\_i)}{k\_i} \tag{6}$$

Here *ηi* is the degradation ration (0.9 for calculation of the EE/O-values) and *ki* reaction rate constant. The first order reaction rate constants for the batch reactor were calculated using the following formula:

$$y = A1\* \exp\left(-\frac{(\mathbf{x} - \mathbf{x}0)}{t1}\right) \tag{7}$$

where *x* is equal to the treatment time within the batch reactor. The correlation was conducted using the Origin Pro software.

In Figure 4, the concentration curves and parameter estimations for the batch reactor are shown exemplary. For carbamazepine, inconsistent concentration curves were measured. As shown in Figure 4A, in two experiments, an increase of concentration occurred during the photocatalytic treatment. These increases may be explained, by the presence of oxcarbazepine, among other predecessor substances, within these wastewater samples. Consequently, carbamazepine was not included in the evaluation of the batch tests. For all reactors, 1st order reaction kinetic was applied and experimental proven. All experimental parameters (Table S1), measured concentrations (Table S2) as well as the corresponding half-live constants and fitting parameters (Table S3) are shown in the Supplementary Materials.

*Water* **2022**, *14*, x FOR PEER REVIEW 8 of 15

plementary material.

corresponding half-live constants and fitting parameters (Table S3) are shown in the Sup-

**Figure 4.** Concentration curves of carbamazepine and concentration curves and first-order fitting curves for benzotriazole, diclofenac, and candesartan for batch reactor experiments. **Figure 4.** Concentration curves of carbamazepine and concentration curves and first-order fitting curves for benzotriazole, diclofenac, and candesartan for batch reactor experiments.

#### **3. Results 3. Results**

In Figure 5, the measured micro-pollutant concentrations in the effluent of the wastewater treatment plant Weimar-Tiefurt are shown. There were high loads of benzotraizole, diclofenac, and gabapentin. The initial concentrations of the flat cell reactor experiments were elevated for amisulpride, benzotriazole, candesartan, and carbamazepine compared to the other samples. Especially the parameters benzotriazole and gabapentin showed high fluctuations. Compared to typical effluent concentrations reported in [2], similar values were measured for all the micropollutants except diclofenac and carbamazepine, which were detected in higher concentrations. Overall, representative micro-pollutant concentrations of the secondary effluent were observed. In Figure 5, the measured micro-pollutant concentrations in the effluent of the wastewater treatment plant Weimar-Tiefurt are shown. There were high loads of benzotraizole, diclofenac, and gabapentin. The initial concentrations of the flat cell reactor experiments were elevated for amisulpride, benzotriazole, candesartan, and carbamazepine compared to the other samples. Especially the parameters benzotriazole and gabapentin showed high fluctuations. Compared to typical effluent concentrations reported in [2], similar values were measured for all the micropollutants except diclofenac and carbamazepine, which were detected in higher concentrations. Overall, representative micro-pollutant concentrations of the secondary effluent were observed.

The photocatalytic reaction constants for all three reactor systems operated with similar electrical irradiation power per hydraulic volume are presented as halftime values in Figure 6. A higher reaction rate of many substances is evident for the flat cell reactor. For benzotriazole and diclofenac, similar degradation rates were measured for flat cell and batch reactor. Similar half time values were achieved for carbamazepine, amisulpride, and gabapentin within flat cell and immersion rotary body reactor. Interestingly, benzotriazole and methlybenzotriazole showed significantly lower degradation affinity within the immersion rotary body reactor. Overall, a reactor- and substance specific reaction behaviour was found.

**Figure 5.** Initial concentrations of micro-pollutants shown as boxplot and individual data point for all experimental steps measured in samples of the secondary effluent of the Weimar-Tiefurt treatment plant (IRB: immersion rotary body reactor; FC: flat cell reactor). **Figure 5.** Initial concentrations of micro-pollutants shown as boxplot and individual data point for all experimental steps measured in samples of the secondary effluent of the Weimar-Tiefurt treatment plant (IRB: immersion rotary body reactor; FC: flat cell reactor). *Water* **2022**, *14*, x FOR PEER REVIEW 10 of 15

**Figure 6.** Halftime constants for the degradation of micro-pollutants within batch, flat cell (FC), and immersion rotary body reactor (IRB) with similar electrical irradiation powers of 9.2 to 1483 kW/m³. **Figure 6.** Halftime constants for the degradation of micro-pollutants within batch, flat cell (FC), and immersion rotary body reactor (IRB) with similar electrical irradiation powers of 9.2 to 1483 kW/m3 .

In Figure 7A the mean 1st order reaction constants for all experimental steps are shown in relation to each electrical irradiation power. Similar to Figure 6, overall higher

lapping behaviour at similar radiation powers for batch and flat cell reactor. The immersion rotary body reactor showed overall lower mean reaction rates. For all reactors, an increase in radiation power was associated with higher degradation rates. For the flat cell, as well as the immersion rotary body reactor, efficiency losses at higher radiation powers can be observed. This effect can be caused by the quantum efficiency of the catalysts, diffusion processes, or substances specific adsorption affinities. In Figure 7B, the influence of varying irradiated catalyst area to hydraulic volume (A/V) ratios was corrected assuming a linear relation to the mean 1st order constants. The A/V values of the batch (30.8 m²/m³), flat cell (90.5 m²/m³), and immersion rotary body (48.9 m²/m³) were scaled to an intermediate value of 50 m²/m³. It is evident that the comparatively high reaction performance of the flat cell reactor is mainly caused by its higher A/V ratio. The reaction rates of flat cell and batch reactor show similar magnitudes; however, for the batch reactor, these are achieved with significantly lower irradiation powers. The immersion rotary body reactor shows overall about a 2–3 times lower reaction rate even at significantly higher irradiation

powers than the batch reactor.

In Figure 7A the mean 1st order reaction constants for all experimental steps are shown in relation to each electrical irradiation power. Similar to Figure 6, overall higher values were achieved for the flat cell reactor. The progression of curves indicates an overlapping behaviour at similar radiation powers for batch and flat cell reactor. The immersion rotary body reactor showed overall lower mean reaction rates. For all reactors, an increase in radiation power was associated with higher degradation rates. For the flat cell, as well as the immersion rotary body reactor, efficiency losses at higher radiation powers can be observed. This effect can be caused by the quantum efficiency of the catalysts, diffusion processes, or substances specific adsorption affinities. In Figure 7B, the influence of varying irradiated catalyst area to hydraulic volume (A/V) ratios was corrected assuming a linear relation to the mean 1st order constants. The A/V values of the batch (30.8 m2/m3 ), flat cell (90.5 m2/m3 ), and immersion rotary body (48.9 m2/m3 ) were scaled to an intermediate value of 50 m2/m3 . It is evident that the comparatively high reaction performance of the flat cell reactor is mainly caused by its higher A/V ratio. The reaction rates of flat cell and batch reactor show similar magnitudes; however, for the batch reactor, these are achieved with significantly lower irradiation powers. The immersion rotary body reactor shows overall about a 2–3 times lower reaction rate even at significantly higher irradiation powers than the batch reactor. *Water* **2022**, *14*, x FOR PEER REVIEW 11 of 15

**Figure 7.** Mean 1st order reaction constant for the degradation of micro-pollutants with the batch, flat cell (FC), and immersion rotary body reactor (IRB) for varying electrical irradiation powers (**A**); mean 1st order reaction constant with a linear correction to a surface area to volume ratio of 50 m²/m³ (**B**) (all mean values without carbamazepine). **Figure 7.** Mean 1st order reaction constant for the degradation of micro-pollutants with the batch, flat cell (FC), and immersion rotary body reactor (IRB) for varying electrical irradiation powers (**A**); mean 1st order reaction constant with a linear correction to a surface area to volume ratio of 50 m2/m3 (**B**) (all mean values without carbamazepine).

The EE/O values were calculated for all experimental steps using the mean 1st order reaction constant of every micro-pollutant. The result is shown in Figure 8 for all micropollutants and the three reactor types at similar radiation power per hydraulic volume. The lowest EE/O values of 5–10 kWh/m³ were achieved for benzotriazole and diclofenac within the batch and flat cell reactor. Within the batch reactor, the other investigated substances were degraded with EE/O values of 10–20 kWh/m³. For the immersion rotary body reactor, overall higher EE/O values were measured ranging from 30 to 60 kWh/m³. The flat cell showed the biggest substance specific range of energy demand with values from 5 to 50 kWh/m³. The EE/O values were calculated for all experimental steps using the mean 1st order reaction constant of every micro-pollutant. The result is shown in Figure 8 for all micropollutants and the three reactor types at similar radiation power per hydraulic volume. The lowest EE/O values of 5–10 kWh/m3 were achieved for benzotriazole and diclofenac within the batch and flat cell reactor. Within the batch reactor, the other investigated substances were degraded with EE/O values of 10–20 kWh/m3 . For the immersion rotary body reactor, overall higher EE/O values were measured ranging from 30 to 60 kWh/m3 . The flat cell showed the biggest substance specific range of energy demand with values from 5 to 50 kWh/m3 .

**Figure 8.** Calculated EE/O value for the photocatalytic oxidation of all micro-pollutants within all

three reactor systems at a radiation power of 9.2–14.8 kW/m³.

**Figure 7.** Mean 1st order reaction constant for the degradation of micro-pollutants with the batch, flat cell (FC), and immersion rotary body reactor (IRB) for varying electrical irradiation powers (**A**); mean 1st order reaction constant with a linear correction to a surface area to volume ratio of 50

The EE/O values were calculated for all experimental steps using the mean 1st order reaction constant of every micro-pollutant. The result is shown in Figure 8 for all micropollutants and the three reactor types at similar radiation power per hydraulic volume. The lowest EE/O values of 5–10 kWh/m³ were achieved for benzotriazole and diclofenac within the batch and flat cell reactor. Within the batch reactor, the other investigated substances were degraded with EE/O values of 10–20 kWh/m³. For the immersion rotary body reactor, overall higher EE/O values were measured ranging from 30 to 60 kWh/m³. The flat cell showed the biggest substance specific range of energy demand with values from

*Water* **2022**, *14*, x FOR PEER REVIEW 11 of 15

m²/m³ (**B**) (all mean values without carbamazepine).

5 to 50 kWh/m³.

**Figure 8.** Calculated EE/O value for the photocatalytic oxidation of all micro-pollutants within all three reactor systems at a radiation power of 9.2–14.8 kW/m³. **Figure 8.** Calculated EE/O value for the photocatalytic oxidation of all micro-pollutants within all three reactor systems at a radiation power of 9.2–14.8 kW/m3 . *Water* **2022**, *14*, x FOR PEER REVIEW 12 of 15

In Figure 9, the mean EE/O values for every experiment are shown in relation to the LED power depicted as the electrical irradiation power per hydraulic volume. For all three reactors, lower EE/O values were archived when using lower radiation lowers and consequentially higher treatment durations. This effect resulted in a 25–45% reduction in energy consumption. Significant differences are evident in the overall energy efficiency of the investigated reactor systems. The batch reactor achieved the lowest EE/O values of 11.8–15.9 kWh/m3 . The EE/O values of the flat cell and immersion rotary body reactor were 2–3 times higher. Analogous to Figure 7A similar values at overlapping radiation powers are indicated by the curve progression of batch and flat cell reactor. In Figure 9, the mean EE/O values for every experiment are shown in relation to the LED power depicted as the electrical irradiation power per hydraulic volume. For all three reactors, lower EE/O values were archived when using lower radiation lowers and consequentially higher treatment durations. This effect resulted in a 25–45% reduction in energy consumption. Significant differences are evident in the overall energy efficiency of the investigated reactor systems. The batch reactor achieved the lowest EE/O values of 11.8– 15.9 kWh/m³. The EE/O values of the flat cell and immersion rotary body reactor were 2– 3 times higher. Analogous to Figure 7A similar values at overlapping radiation powers are indicated by the curve progression of batch and flat cell reactor.

**Figure 9.** Mean EE/O values for the degradation of all eight micro-pollutants within the batch, flat cell, and immersion rotary body reactor as a function of the electrical irradiation per surface area (all mean values without carbamazepine). **Figure 9.** Mean EE/O values for the degradation of all eight micro-pollutants within the batch, flat cell, and immersion rotary body reactor as a function of the electrical irradiation per surface area (all mean values without carbamazepine).

Given the use of similar catalyst material, wastewater samples, and reactor parameters, this result must be significantly influenced by the geometrical arrangement of the catalyst and associated hydraulic impacts. Within the immersion rotary body reactor, the catalyst is only periodically submerged within the medium. The catalyst within the batch

lent state due to the magnetic mixer. In the flat cell reactor, the fluid is forced through the catalysts grid via the pressure gradient of the inlet pump. Based on these results, a submerged arrangement of catalysts proves beneficial in terms of energetic efficiency, whereby no difference could be found between external sources of turbulence and flow

The energy demand for variable mean degradation rates was calculated based on the experimental results of all reactor types (Figure 10). For all reactors, the 1st order constant, which resulted in the lowest EE/O value, was chosen for calculation. Typical treatment target values, as described within the introduction, are indicated graphically. Using the batch reactor, a 75% degradation rate can be achieved with an electrical energy input of 7000 Wh/m³. The flat cell and immersion rotary body reactor had an energy demand of 12,000–14,000 Wh/m³ to achieve a similar degradation. However, following the trend described in Figure 9, values similar to the batch reactor are to be expected when using the

path through the catalysts mesh itself.

flat cell reactor with lower radiation intensities.

Given the use of similar catalyst material, wastewater samples, and reactor parameters, this result must be significantly influenced by the geometrical arrangement of the catalyst and associated hydraulic impacts. Within the immersion rotary body reactor, the catalyst is only periodically submerged within the medium. The catalyst within the batch reactor is permanently submerged. The hydraulic volume below the catalyst is in a turbulent state due to the magnetic mixer. In the flat cell reactor, the fluid is forced through the catalysts grid via the pressure gradient of the inlet pump. Based on these results, a submerged arrangement of catalysts proves beneficial in terms of energetic efficiency, whereby no difference could be found between external sources of turbulence and flow path through the catalysts mesh itself.

The energy demand for variable mean degradation rates was calculated based on the experimental results of all reactor types (Figure 10). For all reactors, the 1st order constant, which resulted in the lowest EE/O value, was chosen for calculation. Typical treatment target values, as described within the introduction, are indicated graphically. Using the batch reactor, a 75% degradation rate can be achieved with an electrical energy input of 7000 Wh/m3 . The flat cell and immersion rotary body reactor had an energy demand of 12,000–14,000 Wh/m3 to achieve a similar degradation. However, following the trend described in Figure 9, values similar to the batch reactor are to be expected when using the flat cell reactor with lower radiation intensities. *Water* **2022**, *14*, x FOR PEER REVIEW 13 of 15

**Figure 10.** Energy demand per volume treated of the batch, flat cell (FC), and immersion rotary body reactor (IRB) in relation to the mean degradation rate of all measured micro-pollutants. **Figure 10.** Energy demand per volume treated of the batch, flat cell (FC), and immersion rotary body reactor (IRB) in relation to the mean degradation rate of all measured micro-pollutants.

Compared to the state of the art, with typical values of 100–300 Wh/m³, further improvement is still needed to achieve a beneficial use of photocatalytic technics. However, compared to the immersion body reactor, significant improvement in energetic efficiency could be achieved at a laboratory scale. When comparing the results to previous measurement of EE/O values within wastewater, similar EE/O values were achieved within the immersion rotary body and flat cell reactor system [20]. The results of the batch reactor test showed an overall improvement in energetic efficiency with EE/O values ranging from 5 to 20 kWh/m³. When comparing the EE/O values aggregated in [12], significantly lower energy demands (up to 240 Wh/m³) were measured for the degradation of various indicator substances (e.g., methylene blue, methylene orange, malachite green). However, a comparability of results is not given due the use of ultra-pure water and the increased degradation affinity of these indicator substances. In Mecha et al. 2017 [28], an energy Compared to the state of the art, with typical values of 100–300 Wh/m3 , further improvement is still needed to achieve a beneficial use of photocatalytic technics. However, compared to the immersion body reactor, significant improvement in energetic efficiency could be achieved at a laboratory scale. When comparing the results to previous measurement of EE/O values within wastewater, similar EE/O values were achieved within the immersion rotary body and flat cell reactor system [20]. The results of the batch reactor test showed an overall improvement in energetic efficiency with EE/O values ranging from 5 to 20 kWh/m3 . When comparing the EE/O values aggregated in [12], significantly lower energy demands (up to 240 Wh/m3 ) were measured for the degradation of various indicator substances (e.g., methylene blue, methylene orange, malachite green). However, a comparability of results is not given due the use of ultra-pure water and the

demand of 38–47 kWh/m³ was determined for the degradation of phenol within second-

In this work, the degradation performance for the photocatalytic oxidation of eight micro-pollutants with real secondary effluent was investigated using three different reactor designs. Flat cell and batch reactor showed similar substance specific degradation behaviors. Within the immersion rotary body reactor, benzotriazole and methyl-benzotriazole showed a significantly lower degradation affinity. The flat cell reactor achieved the highest mean degradation rate due to its high catalysts surfaces to hydraulic volume ratio. The EE/O values were calculated for all micro-pollutants as well as the mean degradation rate constant of each experimental step. The lowest substance specific EE/O values of 5 kWh/m³ were measured for benzotriazole within the batch reactor. The batch reactor also reached the lowest mean values (11.8–15.9 kWh/m³) followed by the flat cell reactor (21.0– 37.0 kWh/m³) and immersion rotary body reactor (23.9–41.0 kWh/m³). Catalyst arrangement and irradiation power were identified as major influences on the energetic

ary effluent using suspended TiO2 catalysts.

**4. Conclusions**

increased degradation affinity of these indicator substances. In [28], an energy demand of 38–47 kWh/m3 was determined for the degradation of phenol within secondary effluent using suspended *TiO2* catalysts.

### **4. Conclusions**

In this work, the degradation performance for the photocatalytic oxidation of eight micro-pollutants with real secondary effluent was investigated using three different reactor designs. Flat cell and batch reactor showed similar substance specific degradation behaviors. Within the immersion rotary body reactor, benzotriazole and methyl-benzotriazole showed a significantly lower degradation affinity. The flat cell reactor achieved the highest mean degradation rate due to its high catalysts surfaces to hydraulic volume ratio. The EE/O values were calculated for all micro-pollutants as well as the mean degradation rate constant of each experimental step. The lowest substance specific EE/O values of 5 kWh/m3 were measured for benzotriazole within the batch reactor. The batch reactor also reached the lowest mean values (11.8–15.9 kWh/m3 ) followed by the flat cell reactor (21.0–37.0 kWh/m3 ) and immersion rotary body reactor (23.9–41.0 kWh/m3 ). Catalyst arrangement and irradiation power were identified as major influences on the energetic performance of the reactors. A high catalyst surface to hydraulic volume ratio, which led to high degradation rates within the flat cell reactor showed no beneficial impact on energetic efficiency. A treatment according to existing treatment goals of wastewater treatment plants can be achieved using the batch reactor with a calculated energy demand of 7000 Wh/m3 . Compared to existing literature values, an improved energetic efficiency was measured for the batch reactor configuration. This highlights the potential of this reactor type within photocatalytic wastewater treatment. However, a major difference compared to the energetic efficiency of ozonation and activated carbon still persists.

As mentioned in the introduction, the batch and flat cell reactor types were only developed on a laboratory scale. Especially in terms of radiation, transport optimizations, such as equalizing radiation fields, minimizing absorption of the liquid and reflections, are possible. Therefore, further research work regarding these reactor types is necessary. Following the results of [29], further increases in efficiency seem possible, when integration controlled periodic illumination (CPI) within the reactor systems. When comparing the energy demand with the state of the art (especially ozone oxidation), it also needs to be mentioned that a direct comparability is not given due to deviating oxidation, transformation, and mineralization characteristics of each reaction cascade. The beneficial effect of AOP on the formation of transformation products as described in [30] needs to be seriously considered when comparing treatment options.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/w14172681/s1, Table S1: Experimental program; Table S2: Concentrations; Table S3: Kinetics.

**Author Contributions:** Conceptualization, S.M.; methodology, S.M. and T.S.; investigation, S.M.; data curation, S.M.; writing—original draft preparation, S.M. and T.S.; writing—review and editing, S.M.; supervision, J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Federal Ministry of Education and Research grant number [02WQ1492A].

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Supporting data can be found in Supplementary Materials.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


3.2.1. Overall Estimates

Over the period 2004–2009, the number of deaths attributable to NO2 exposure was 4301 (95% CI [2044; 6545]), which corresponds to about 717 death per year (95% CI [340; 1091]) and to a rate of 52.8 (95% CI [25.1; 80.3]) per 100,000 inhabitants (aged > 30 years). It represents about 5% of total deaths among the adult population over 30 years of age.

For particulate matter, over the period 2007–2009, the number of attributable deaths was equal to 3209 (95% CI [1938; 3355]) and 2,662 (95% CI [2859; 3553]) for PM10 and PM2.5, respectively. This corresponds to a rate of 78.2 (95% CI [69.7; 86.6]) and 64.9 (95% CI [44.5; 84.4]) per 100,000 inhabitants (about 7.8% and 6.5% of total deaths for PM10 and PM2.5, respectively).

### 3.2.2. Estimates by Socioeconomic Deprivation Class

Tables 5 and 6 show the rate of attributable deaths estimated for the three air pollutants by decile of the socioeconomic deprivation index distribution.

### **Table 5.** Rate of attributable deaths per socioeconomic class for two dose-response function.

Dose response function 1 based on Dutch study [14] and dose response function 2 based on Italian study [13]. 95% CI: 95% Confidence Interval.

**Table 6.** Rate of attributable deaths per socioeconomic class for two dose-response function.

Dose response function 1 based on Dutch study [14] and dose response function 2 based on Italian study [13]. 95% CI: 95% Confidence.

Whatever the pollutant, the most deprived census blocks (decile 10) always appeared as one of the groups most impacted by air pollution. With an annual average of NO2 equal to 54.11 μg/m3 (one of the highest values), the attributable death rates are estimated to be 45.2 and 49.4 per 100,000 inhabitants using the dose-response function of the Dutch and Italian studies, respectively. With an annual average of PM10 and PM2.5 equal to 31.17 μg/m3 and 20.84 μg/m3 (in the high range of the

annual average of air pollutants), the attributable death rates are estimated to be 100.1 and 54.0 per 100,000 inhabitants, using the dose-response function of the Dutch and Italian studies, respectively.

Populations living in less deprived census blocks (decile 3 and 4, in particular) also appear highly impacted by air pollution. These findings are consistent with the increase of the dose-response function and the level of air pollutant exposure in the high range, whatever the pollutant of interest.