**1. Introduction**

The fertilizer industry faces a continuing challenge to improve its products and increase the efficiency of their use, particularly of nitrogenous fertilizers, and to minimize any possible adverse environmental impact. This is done either through improvement of fertilizers already in use, or through development of new specific fertilizer types [1]. One specific objective in Europe is the development of new sustainable fertilizers with the aim of reducing the consumption and/or import of conventional mineral fertilizers from third-world countries, and of N fertilizers based on energy-intensive production process. The development of innovative controlled-released fertilizers (CRFs) could limit the consumption of the traditional ones. Indeed, CRFs present the interest to better match the plants' need for nutrients over time.

Several CRFs products are already available in the market. They are manufactured from blends of fertilizers as active principle and organic polymers. There are two major CRFs types [2]. In the first, the fertilizer is encapsulated into the polymer matrix. In the second, the fertilizer is coated by a polymer layer. The fertilizer bioavailability for the plant uptake is controlled by the diffusion rate through the pores of the polymer matrix or through the surrounding polymer shell. Although several commercial CRFs can achieve

**Citation:** Evon, P.; Labonne, L.; Padoan, E.; Vaca-Garcia, C.; Montoneri, E.; Boero, V.; Negre, M. A New Composite Biomaterial Made from Sunflower Proteins, Urea, and Soluble Polymers Obtained from Industrial and Municipal Biowastes to Perform as Slow Release Fertiliser. *Coatings* **2021**, *11*, 43. https://doi.org/10.3390/coatings11010043

Received: 17 December 2020 Accepted: 30 December 2020 Published: 2 January 2021

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**Copyright:** © 2021 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/).

satisfactory fertilizers release rate, the plastic polymer in the blend raises concern for its low biodegradability and the accumulation of plastic impurities in soil. The new EU regulation on fertilizing products addresses the degradability of the organic matter of CRFs by stipulating 90% conversion of the organic carbon into CO2 in a maximum of 24 months. The specific challenge focuses on finding organic polymers that comply with the biodegradability EU requirements, while achieving the controlled release of nutrients in the best possible manner.

Much research work has been carried out to develop new CRFs containing natural polymers, instead of synthetic polymers. The substitution of synthetic polymers derived from fossil sources with natural polymers is a current major environmental issue [3]. Although biodegradable polymers can be also synthesized from monomers obtained from the petrochemical industry, the consumption of chemicals from fossil sources is a major source of GHG emission. In addition, it contributes to the depletion of fossil sources. Natural polymers such as lignin, starch, chitin, cellulose, and other polysaccharides (e.g., *k*-carrageenan) have been investigated to make CRFs. Generally, these cannot be used in natural pure form as effective coating material due to their inherent properties, such as poor mechanical properties, hydrophobicity, high industrial demand, poor solubility, and processability. These imply limitations for their use.

To overcome the above criticalities, researchers have focused their work on chemical and physical modifications of natural polymers. Biopolymers from monomers obtained from renewable source have also been obtained, e.g., polylactic acid (PLA) or poly hydroxyl alkanoates (PHA). Aims of current research on CRFs are to reduce production cost, improve nutrient release rate, optimize organic matter biodegradability rate, and guarantee crushing strength to bear crack resistance under environmental stresses. These three properties need to be compatible with each other. Indeed, a poor mechanical strength or high biodegradability may result in faster nutrient release rate, excess nutrient availability over the plant uptake rate, high nutrient leaching from soil into ground, and low CRFs efficiency. High crushing strength also facilitates the handling and transportation and reduces the water absorption [4].

In the present work, the current CRFs issues have been addressed by fabricating a new material out of three components. These are a sunflower protein concentrate (SPC) obtained from sunflower oil cake (SOC), a biopolymer (BP) obtained from municipal biowaste (MBW), and commercial urea (U). All three components contain N. Three different blends were manufactured, i.e., SPC-10% U, SPC-10% BP, SPC-5% U-5% BP. The underlying rationale of the work was to use SPC as polymer matrix, and BP and U as fillers/active principles. The reason for this approach is based on the following literature data reported for SPC, BP and U.

The SPC is well known biopolymer used in human nutrition for its nutritional value [5], and potential antioxidant properties and benefits for disease prevention and aging retardation [6]. SPC are reported also to have also good processability to manufacture films by solvent casting [7] and extrusion at 160 ◦C [8]. This material therefore seemed a potential eco-friendly matrix to be used for manufacturing new CRFs.

The BP is obtained by chemical hydrolysis of fermented unsorted urban food wastes. The BPs sourcing food wastes contain all major animal and vegetable natural polymers. Upon anaerobic fermentation, the most readily degradable fats, polysaccharides, and proteins are converted to methane and carbon dioxide. The solid anaerobic digestate (AD) contains the recalcitrant lignocellulosic fraction. The BP, obtained from AD chemical hydrolysis, contains a mix of water-soluble molecules with molecular weight from 5 to above 750 kDa [9]. These molecules are water soluble lignocellulosic fragments. They keep the memory of the macromolecularity and functional groups, and of the mechanical properties of the proximates in the parent food wastes. However, BP is water soluble and thermally stable up to 200 ◦C. This allows for fabricating, by solvent casting and melt extrusion, composite articles containing BP in blends with synthetic polymers such as polyethyleneco-vinyl alcohol [10] and polyethylene-co-acrylic acid [11]. The blends have been proven

to have higher mechanical resistance, compared to the article manufactured from the neat synthetic polymer only. The BP is not biodegradable. However, based on its sourcing material, no adverse effect is expected from its accumulation in soil. Indeed, MBW anaerobic digestates and/or compost are used as soil amending agents and/or fertilizers. The BP has also been proven [9] more efficient soil fertilizer and plant growth biostimulant in the cultivation of several food and ornamental plants, in comparison with its sourcing fermented MBW materials and with commercial organo-inorganic peat and leonardite derived products claimed by the vendor as plant biostimulants. This material seemed therefore a potential processable filler, capable of increasing the mechanical resistance of the SPC matrix and to perform at the same time as active fertilizer principle in CRFs.

Urea is a main commercial fertilizer. Its world consumption amounts to approximately 51 Mt/yr [12]. In the present work, urea has been chosen as reference commercial N fertilizer. Urea and lignin materials have also been investigated for use in the CRF field. Urea coated with pine lignin [13] mixed with various types of additives exhibited 59% higher crushing strength than the uncoated fertilizer. Urea coated with four types of lignin, recovered from the effluent liquor of the paper and paper industry [14], gave products with film forming properties, but unsatisfactory release rate. The same occurred for urea granules fabricated by mixing urea with kraft lignin under melting conditions [15]. Kraft lignin was added to a tapioca starch-urea-borate matrix to modulate the starch matrix hydrophilicity properties and reduce the urea release in water [16]. The final film product remained intact after one month of contact time with water. The retardation of the urea release rate by Kraft lignin was confirmed also for other formulations [15]. Based on these results, the BP keeping the memory of the pristine lignin matter present in the sourcing food waste seemed to the authors of the present work to offer intriguing perspectives for use together with urea and the SPC matrix in the manufacture of new CRFs.

The main aims of the present work were to develop a twin-screw extrusion process tailored to the fabrication of the new SPC-BP-U pellets and to test the pellets mechanical behavior and release rate in solution of organic and inorganic nitrogen. The principal conclusions of the work are that the developed twin-screw extrusion process successfully produces the SPC-BP-U pellets and that the pellets behave as CRFs. The results also suggest that BP has positive effects on the mechanical properties of the pellets and on the chemical behavior of the organic and inorganic N species released in solution.

#### **2. Materials and Methods**

#### *2.1. SPC, BP, U, and Other Reagents*

The BP was available from previous work [9]. It was obtained from the anaerobic digestate of the MBW processed in the ACEA waste treatment plant in Pinerolo, Italy. The sampled digestate was hydrolyzed at 60 ◦C in pH 13 water. The liquid hydrolysate was separated from the insoluble residue by centrifugation and then filtered through polysulphone membrane with 5 kDa cut off. The membrane retentate was dried at 60 ◦C to yield the solid BP.

The SPC was obtained from a sunflower oil cake (SOC). To obtain it, SOC was sieved using a Ritec (France) 600 vibrating sieve shaker fitted with a 1 mm grid. The oversize was mainly composed of solid particles from the seed hull, and therefore rich in fibers. Conversely, the undersize (SPC) was enriched with the smaller particles coming from the kernel of the seed, thus having a high protein content. This resulted in a higher content of SPC in proteins, the latter having been evaluated at 50.7% ± 0.1% (in proportion to the SPC dry weight) using the Kjeldahl method [17]. The other chemicals inside SPC were minerals, lipids, cellulose, hemicelluloses, lignins, and water-solubles, with contents of 8.6% ± 0.1%, 1.4% ± 0.1%, 11.0% ± 0.9%, 11.2% ± 1.7%, 0.8% ± 0.2%, and 26.3% ± 0.1%, respectively. The SPC components were determined according to the following literature methods: ISO 749 standard [18] for minerals, ISO 659 standard [19] for lipids, and the ADF-NDF method [20,21] for cellulose, hemicelluloses, and lignin. The content in watersoluble components was estimated by measuring the mass loss of the test sample after 1 h in boiling water. All analyses were carried out in duplicate.

#### *2.2. Fabrication of Extruded Pellets and Injection-Molded Pieces* 2.2.1.ExtrudedPellets

The extruded pellets were obtained by destructuring and then plasticizing the proteins present in SPC. Thermo-mechano-chemical destructuring was carried out [22,23]. Protein plasticization was conducted in the presence of an aqueous solution of sodium sulfite (1 kg sodium sulfite for 10 kg water) or even urea (U) using a Clextral (France) Evolum HT 53 co-rotating and co-penetrating twin-screw extruder (Figure 1). The latter was composed of nine modules, each 4D in length, D corresponding to the screw diameter (i.e., 53 mm). The total barrel length was thus 36D (i.e., 1.908 m).

The screw rotation speed was 300 rpm for all the formulations produced. The temperature profile along the barrel was 20 ◦C for the feeding module (module 1), 80 ◦C for module 2, and 100 ◦C for the seven other modules (i.e., modules 3 to 9).

SPC was introduced at the level of module 1 using a Coperion K-Tron (Coperion K-Tron (Schweiz) GmbH, Niederlenz, Switzerland) SWB 300-N weight feeder at a 64.5 kg/h inlet flow rate. The sodium sulphite solution was injected at the end of the second module at a 23.3 kg/h inlet flow rate using a DKM (Clextral, Firminy, France) Super K CAMP 112 piston pump. The resulting formulation was referred to as SPC. When incorporated in the formulation, BP and U were also introduced at the level of module 1 using a Coperion K-Tron K-ML-KT20 weight feeder (Coperion K-Tron (Schweiz) GmbH, Niederlenz, Switzerland) at a 6.5 kg/h inlet flow rate (i.e., 10% (*w*/*w*) in proportion to SPC). The corresponding formulations have been designated as follows: SPC-BP and SPC-U, respectively. For its part, the SPC-BP-U formulation was obtained by adding simultaneously BP and U in module 1, both introduced at a 3.25 kg/h inlet flow rate.

The intimate mixing of the solids each other, and the impregnation of the liquid into the solid(s) was made possible by the use of two consecutive pairs of bilobe paddles (BL22 type), each 1D in length, positioned at the beginning of module 4. The plasticization of sunflower proteins was obtained through intense mechanical shear using four consecutive pairs of reversed elements (CF2C type), each 0.5D in length, positioned at the end of module 7 and beginning of module 8. A die equipped with six holes, each 3 mm in diameter, was positioned at the end of the barrel, and the extruded pellets were obtained using a Clextral HC 45 granulating system. They were then dried using a Clextral Evolum 600 continuous belt dryer up to a 10% moisture content before their packaging inside sealed plastic bags.

#### 2.2.2. Injection-Molded Pieces

The extruded pellets were molded into standard bending and tensile specimens by thermoplastic injection using a Negri Bossi (Cologno Monzese, Italy) VE 160–720 machine with a clamping force of 160 ton, and a mold with two cavities. All formulations were rehydrated by adding water to the extruded pellets prior to thermoplastic injection. The moisture content of the extruded pellets was 20% at the time of molding.

The conditions used to produce the test specimens were as follows: 70 ◦C/90 ◦C/110 ◦C for the temperature profile along the plasticizing screw, 130 ◦C for the temperature of the nozzle, 150 rpm for the rotation speed of the plasticizing screw up to a 21 mm length for the shot building, 30 bar for the counter pressure, 150 mm/s for the injection speed, 800 bar for the follow-up pressure applied during a 2.5 s duration, 1600 kN for the clamping force, 50 ◦C for the mold temperature, and 20 s for the cooling time before opening the mold and ejecting the test specimens from the cavities.

Once obtained, the test specimens were placed in a climatic chamber at 60% relative humidity (RH) and 25 ◦C for three weeks for equilibration. Once equilibrated, they were then used for characterization.

#### *2.3. Characterization of Mechanical Properties and Water Sensitivity Test for SPC Composites* 2.3.1. Density of Extruded Pellets and Injection-Molded Pieces

The density of the extruded pellets was measured using a 50 mL pycnometer, and cyclohexane as immersion liquid. Cyclohexane was chosen because of its marked hydrophobic character. Indeed, cyclohexane contains only carbon and hydrogen atoms whose bonds are not polarized, thus classifying this solvent in the category of the apolar solvents. On the contrary, the granules are hydrophilic as they are composed of biomolecules and biopolymers with many polarized bonds (e.g., CO and NH bonds for proteins, OH bonds for cellulose, etc.). In consequence, there is no absorption of cyclohexane by the granules and no change in their volume by swelling. All determinations were carried out in duplicate.

The density of the injection-molded pieces was measured from three test specimens with an 80 mm length, a 10 mm width, and a 4 mm thickness. Their thickness and their width were measured at three points, and their length at two points, with a 0.01 mm resolution electronic digital sliding caliper. Thickness (*t*), width (*b*), and length (*l*) mean values were recorded to calculate the specimen volume, and test specimens were all weighed to calculate their density (*d*). Mean apparent density of the injection-molded pieces from the same formulation was the mean value of measurements made on the three test specimens.

#### 2.3.2. Resistance to Mechanical Abrasion

The resistance to mechanical abrasion of both pellets and injection-molded pieces was estimated from an unstandardized test that was specifically developed during this study. For this test, a cylindrical plastic container having a 115 mm diameter and a 130 mm height was used. The bottom of the container was fixed to an axis, inclined at 15◦ to the top with respect to the horizontal direction, and rotating by means of a motor at an 80 rpm rotation speed. About 30 g of pellets or injection-molded pieces having a 10 × 10 × 4 mm3 volume were positioned inside the container before the beginning of the test. During the test, 10 metal parts for a total mass of 125 g were also placed inside the container, their addition aiming to simulate the mechanical abrasion to which the pellets or the injection-molded pieces will be subjected during their spreading to the field. Two wooden blades having the same length as the height of the container, both 1 cm high and 1 cm wide, were fixed inside the cylinder, 180◦ to each other. Here, their objective was to amplify the effect of mechanical abrasion on the tested sample by the metal parts. The test duration was 1 h. At the end of the test, the plant objects were recovered and then sieved on a 2 mm grid to quantify the fines generated during the mechanical abrasion test. The result was expressed as the ratio of fines to the initial mass of the sample analyzed (%). The higher this ratio, the more sensitive the sample was to mechanical abrasion. All determinations were carried out in duplicate.

#### 2.3.3. Tensile and Bending Properties

The tensile properties of the injection-molded pieces were determined according to ISO 527-4 standard [24]. In particular, the two points chosen at the beginning of the stress-strain curve for the Young's modulus calculation were associated with the following elongations: 0.0005 (i.e., 0.05%) and 0.0025 (i.e., 0.25%).

The flexural properties were determined according to the ISO 178 standard [25], i.e., using the three points bending method. For both tensile and bending properties, an Instron (Instron, Norwood, MA, USA) 33R 4204 universal testing system fitted with a 5 kN load cell was used.

The distance between jaws for the tensile tests was 105 mm, and a testing speed of 3 mm/min was applied. The grip separation for the bending tests was 40 mm, and a testing speed of 2 mm/min was applied. All determinations were carried out through four repetitions.

#### 2.3.4. Shore D Surface Hardness

The Shore D surface hardness of the injection-molded pieces was determined according to the ISO 868 standard [26] with a Bareiss (Bareiss Prüfgerätebau GmbH, Oberdischingen, Germany) durometer. The indentation direction was perpendicular to the upper face of the injection-molded piece. For each formulation, determinations were carried out from four different bending test specimens, and measurements were made 10 times per each test specimen (five times for each of its sides).

#### 2.3.5. Water Sensitivity

The water sensitivity of the injected pieces was determined according to ISO 16983 standard [27]. Measurements were conducted after soaking of the analysed samples in water during 1, 3, 6, and 24 h, respectively. Thickness swelling (TS, %) and water absorption (WA, %) were calculated. All determinations were carried out in triplicate.

#### *2.4. Measurements of N Release from SPC Composites*

The study of the release of urea and other N-containing compounds was performed according to a previously reported procedure [1]. The samples (4 g each) were immersed in 80 mL demineralized water in a 100 mL glass flask. Three replicates were carried out for each material. The flasks were stored at 25 ◦C in the dark. At given sampling times (1, 2, 4, 10, 15, 25 days), the liquid and solid phases were separated by centrifugation (15 min at 3000 rpm). The liquid phase was used for the determination of the concentrations of urea, ammonia, and total nitrogen with the methods described below. At each sampling, the same amount of demineralized water as that of the discarded supernatant was added to the solid phase.

#### 2.4.1. Determination of the Urea Concentration

The concentration of urea in the supernatant was measured following the spectrophotometric method of Chen et al. [28]. One ml aliquot solution was diluted with water (1:1000 (*v*/*v*) after 1 day extraction, 1:500 (*v*/*v*) after 2, 4, and 7 days of extraction, 1:100 (*v*/*v*) after 10 days of extraction, and 1:50 (*v*/*v*) after 15 and 25 days of extraction).

To the diluted samples (18 mL), transferred in a 100 mL amber glass bottles, 0.9 mL di DAM (diacetylmonoxime 0.5 g/10 mL), 0.15 mL TSC (thiosemicarbazide 0.2 g/100 mL), 0.15 mL FeCl3, 6H2O 40.56 mg/100 mL), 12 mL (5%, *v*/*v*) H2SO4 were added in sequence. The reaction mixtures were heated in a water bath at 80 ◦C for one hour.

The absorbance of the complex was measured at 520 nm in a spectrophotometer (Hitachi U-2000), previously calibrated with solutions of urea at given concentrations treated as described above.

#### 2.4.2. Determination of Ammonia Nitrogen

A 10 mL aliquot of the supernatant was transferred to a Kjeldahl tube. After addition of 1 g MgO and 70 mL water, NH3 was distilled in a Kjeldahl instrument and collected in a beaker containing 20 mL boric acid and 2 drops of methyl red and bromocresol green indicator.

#### 2.4.3. Determination of Total Nitrogen

A 1 mL aliquot of the supernatant was suspended in 25 mL sulfuric acid and 0.5 g selenic mixture and heated to boiling point until complete mineralization. The solution was transferred to a 25 mL volumetric flask and brought to volume with water. A 10 mL aliquot was transferred to a Kjeldahl tube and treated as above after addition of 40 mL 40% NaOH instead of MgO.

#### 2.4.4. Determination of Organic Nitrogen

Organic nitrogen was calculated by subtracting ammonia and urea N from total N.

#### *2.5. Statistical Treatment of Data*

The data were evaluated by one-way ANOVA (*p* < 0.05) followed by the Tukey's test for multiple comparison procedures.


*3.4. CSO-ELM Model*

In the basic ELM model, when the number of hidden layer nodes is fixed, the input weights and hidden layer deviations of the network structure are randomly determined, which affects the stability of the model itself. After using CSO to improve ELM algorithm, the number of hidden layer nodes, input weights and thresholds value of ELM model are adaptively selected. The flow chart is shown in Figure 1. In the CS algorithm, *N* nests are set. Then, round the position value as the number of the hidden nodes *M*. After inputting the sample data, a set of input weight matrices *W* for ELM networks will be generated. Then, the threshold value of the hidden layer *b* will be obtained, and the model output the weight matrices β. The prediction error calculated by the sample data will be the fitness value of the present nest. At the end of each iteration, the optimal nest location is preserved, and the values of *W*, *b*, and β are outputted. After satisfying the iteration termination conditions, the fitness values of nests are compared. Finally, the optimal position of the bird's nest are obtained, and the corresponding values of *W*, *b*, β will be exported. The improved ELM method operates as follows.

**Figure 1.** Outline of procedure of proposed method.

### **4. Case Study and Results**

#### *4.1. Validation Test of Forecasting Model*

The proposed forecasting method is tested by historical data inputting. The data used in this research is collected from both the "Statistical Yearbook of China" and the "Annual Report of Electric Power Industry" for the latest 20 years. Given the development of electric power substitution projects being affected by many factors, Pearson correlation test is carried out for test the correlations of each factor. Factors with high correlation are used as input data of the forecasting model to accurately predict the market potential of electric power substitution projects. The historical data are shown in the Table 3. The correlation test of each factor is carried out by Statistical Product and Service Solutions (SPSS) software and the results are shown in Table 3 and Figure 2.

**Figure 2.** Pearson coefficient.

The research is used to forecast the market potential of power substitution projects in 2018–2030. A forecasting model of extreme learning machine based on the cuckoo search optimization is constructed. By taking historical data of 1998–2017 as an example, the validity of this model is tested. Due to the substitution work being a gradual process—when the CSO-ELM model is used to forecast the market potential of electric power substitution projects—the electric power substitution amount in every year in the future is forecasted in turn. When forecasting, the data of the target year is added to the training set of the model to give out more accurate results.

Data from 1998 to 2012 is chosen as the training set, and data from 2013 to 2017 is used as the test set. The BP neural network, ELM and CSO-ELM forecasting models are all used to forecast the electric power substitution from 2013 to 2017. ELM and BP neural network are introduced as the contrast algorithm, and the parameters are set to be at the optimal values after comparison. In setting the parameters settings of BPNN, the number of neurons in hidden layer is set as 6. In addition, the tansig function is used as the transfer function. The output layer is set as the purelin function. The training times are set to 1000, and the precision target is set as 0.001. In the ELM forecasting model, the number of nodes in hidden layer is 30, and the activation function is sig function. The initial population number of CSO-ELM is 20 and the maximum iteration number is 200. Historical data is listed in Table 4.

The fitting results of the three methods are shown in Figure 3. The overall fitting effect of ELM is better than that of BPNN, which is mainly because the forecasting effect of BPNN largely depends on the quality of historical data and the training of a large number of data. The ELM method is an improved BPNN, which maintains the advantages of the fast learning speed and strong generalization ability, and can accurately analyze in the case of small amount of data. The CSO-ELM method combines the global optimization ability of CSO and optimizing transmission weight of ELM, contributing to a better forecasting effect.

**Figure 3.** Results of three forecasting models.

To quantify the effect of the forecasting model, Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and Determining Factor *R*2 are selected as indicators to evaluate the effect of the forecasting model. The calculation of the four error indicators are as follows.

$$RMSE = \sqrt{\frac{1}{n} \sum\_{i=1}^{n} (p\_i - p\_i')^2} \,\tag{15}$$

$$MAPE = \frac{1}{n} \sum\_{i=1}^{N} \left| \frac{p'\_i - p\_i}{p\_i} \right| \times 100\% \tag{16}$$

$$MAE = \frac{1}{n} \sum\_{i=1}^{N} |p'\_i - p\_i| \tag{17}$$

*R*2 = *n i*=1 *p i* − *pi* 2 *n i*=1 *pi* − *pi* 2 , (18)

where, *pi* represents the actual electric power substitution amount. *p i* is the forecasting amount. *pi* is the average of the actual electric power substitution amount, and *n* is the data size. The calculation results of four indicators are shown in Table 5 and the contrast is shown in Figure 4.

**Table 5.** Calculation results of error indicators.

**Figure 4.** Errors of three methods.

From Table 5 and Figure 4, the constructed CSO-ELM model has a better fitting effect, higher prediction accuracy and less error in the market potential forecasting of electric power substitution. Therefore, using CSO-ELM model to forecast the substitution potential in the next 13 years can reflect the future market potential of electric power substitution projects.

#### *4.2. Scenarios Setting*

In forecasting the developing potential of future electric power substitution projects, factors such as the costs, policy support and subsidy mechanism are fully considered. Thus, combined with the political objectives of energy-consumption structure optimization, energy conservation and emission reduction, and renewable energy development, four scenarios are designed: basic scenario, high-cost restraint scenario, policies-supported scenario and subsidy weakening scenario.

In basic scenario, according to the development trend of power substitution related factors in 1998–2018, the market development potential of electric power substitution in 2019–2030 is forecasted. In the high-cost restraint scenario, the substitution project will be slowed down by the purchase of expensive equipment and high operating costs. In the policies-support scenario, in order to reduce the cost resistance and alleviate the environmental pressure caused by fossil energy combustion, the government encourages the substitution work by policy mechanism. China's "13th Five-Year Plan for Electric Power Industry" proposed to encourage the developing of electric power substitution and clean energy substitution to expand the proportion of electricity in energy consumption. In addition, the substitution work is supported with appropriate subsidies mechanisms. Relevant subsidy mechanisms can stimulate the rapid development of electric power substitution projects. With the popularization of electric power substitution, the cost would be gradually recovered, and the projects may be profitable, which is when the subsidy mechanism can be weakened or cancelled accordingly, to achieve an independent development of the substitution projects.

In addition, considering the stage characteristics of social and economic development, the forecasting period is divided into three stages: the first stage (2019–2020), the second stage (2021–2025) and the third stage (2026–2030). The parameter settings for each scenario and time period are shown in the Table 6 below.

will slow down to about 3.2% to

4.3%.

 **6.** Scenarios and primary parameter settings.

**Table**

5.2% to 6.1%. According to the carbon

While high cost brings resistance

to the

energy, it will also weaken the

environmental

reduce the e emission reduction indirectly. The trend of emission reduction

is about 1.4% to 1.7%.

 benefits and fficiency of carbon

development

 of renewable

The renewable energy will

effectively reduce carbon

emissions. Therefore, in this

scenario, the reduction rate

of carbon emissions is about

3.6% to 4%.

development

 of The decreasing rate is

slightly lower than that in

policies-supported

at about 3.6% to 3.8%

 scenario,

emission situation in the

past 20 years, assume that

the trend of carbon emission

reduction in the future is

about 2.1% to 2.5%.

**Carbon Emissions**

### *4.3. Results of Scenarios and Discussion*

In the four different scenarios, the amount of electric power substitution shows significant growth trends before 2030. Affected by electricity consumption, renewable energy generation and other factors, the market potential of electric power substitution has broad market prospects. Electric energy can effectively reduce carbon emissions to a minimum through scale-effect and technological means (such as smart grid) in power production and transmission section. Therefore, the realization of electricity substitution is a low-carbon energy development and utilization strategy as a whole, which will inevitably have a positive impact on China's low-carbon economy. Restricted by high construction costs and operation fees, people's subjective acceptance of electric power substitution project is relatively low, which brings difficulties in popularizing. The forecasting results are shown in Table 7.

**Table 7.** Multi-scenarios forecasting result.

In all four scenarios, substituting electricity shows significant increase. In the first stage, the growth rate is relatively small. During this period, the subsidies of the subsidy weakening scenario have not been weakened, so there is little difference with other scenarios. In the second stage, due to social development and technological progress, electric power substitution projects begin to have a certain scale of promotion. The substitute electricity in the high-cost restraint scenario shows the slowest increase. In the policies-supported scenario, the substitute electricity amount shows accelerated growth, while the subsidy mechanism in the subsidy weakening scenario begins to fade down slightly. In the third stage, after the promotion of the first two stages, the electric power substitution project has a certain scale effect, and the electric power substitution quantity shows a trend of accelerating growth.

As can be seen in Figure 5, the process of electric power substitution in the policies-support scenario and subsidy weakening scenario is significantly higher than that in the basic scenario, which shows that the government support is very important in substitution promoting work. In addition, the substitution amount in the high-cost restraint scenario is the least one in all four scenarios, about 538.17 million tons of standard coal in 2030. High costs lower the residents' acceptance of projects, and the promotion power of manufacturers is insufficient without any subsidies given. In the policies-support scenario, the government supports by giving subsidies in equipment purchasing, installation, operation and maintenance process—so that the projects are promoted—with the most increment in electricity amount, reaching 693.8 million tons of standard coal in 2030. Differing from the policies-support scenario, changes after power substitution reaching certain scale effects are considered in the subsidy weakening scenario. With the popularization of electric power substitution projects and technical progress, government support gradually reduced to raise independence of the substitution industry, as can be seen from the curve of the subsidy weakening scenario in Figure 5. The increasing trend of substitute electricity under the subsidy weakening scenario is similar to that of the policies-support scenario. With the reduction of subsidies, the amount of electric power substitution become less than that in the policies-support scenario after 2023, but still higher than that in the basic scenario, which shows the effectiveness of subsidy weakening.

**Figure 5.** Forecasting results of electric power substitution under all scenarios (million tons of standard coal).

In the early stage of developing electric power substitution, the main fields for promotion are concentrated on substitutions of coal, coal-fired boilers and coal-fired heating—where technology is relatively mature. In the middle and later stages, electric power substitution work turns to household electrification, substitution to internal-combustion engines by electricity and substitution to oil-fired vehicle by electric vehicle. Implementing electric power substitution will bring significant changes in China's energy consumption structure, significantly reducing the proportion of oil and natural gas consumption and significantly reduce carbon emissions.

#### **5. Conclusions**

Electric power substitution is important in environmental management and for optimizing energy structure. Market forecasting on electric power substitution projects is conducive to the government and society to develop and promote the substituting work. Therefore, a market potential forecast model of electric power substitution based CSO-ELM method is proposed. Firstly, the validity of the influencing factors on the potential of power substitution development are verified through correlation test. Secondly, combined with CSO algorithm, the ELM method is improved, which overcomes the limitation of the search ability of the original ELM and improves the forecasting accuracy. In addition, compared with BP, ELM and other forecasting models, the proposed CSO-ELM model has lower error and deviation, showing better forecasting effect. Due to government support and the limitations of industrial development, four scenarios were designed to give specific forecasting results to show how different factors influence the development of electric power substitution projects. In addition, the forecasting time period was divided into three stages to take on further analysis. Through comparative analysis of the forecasting results under different scenarios, some suggestions for promoting electric power substitution are proposed below.

**Author Contributions:** Conceptualization, J.W. and G.D.; methodology, K.W.; software, K.W.; validation, G.D., Z.T. and L.P.; formal analysis, J.W.; investigation, Q.T.; resources, L.J.; data curation, K.W.; writing—original draft preparation, J.W.; writing—review and editing, G.D. and Q.T.; K.W.; supervision, Z.T.; project administration, G.D. and L.J.; funding acquisition, Z.T. and L.J.

**Funding:** This work was partially supported by Supported by the Project funded by China Postdoctoral Science Foundation (2019M650024), the National Nature Science Foundation of China (Grant Nos. 71904049,71874053, 71573084), the Beijing Social Science Fund(18GLC058) and the 2018 Key Projects of Philosophy and Social Science Research, Ministry of Education, China (18JZD032).

**Acknowledgments:** Teachers and classmates helped complete this paper. We would like to express our gratitude to them for their help and guidance.

**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/).


**2.2 Import pictures**

Dim and noisy images were imported into Netlogo using the command "importpcolors", which transformed the luminance values of pixels into the grey values of patches. This Netlogo function limits the luminance values of grey-scale images to the range between 0 (black) to 9.9 (white), whereby the total number of possible grey values is 110. Although image information is reduced by this function, the human eye is unable to recognize any difference between images having a grey value range of 110 or higher.

## **2.3 Image statistics**

For the automatic adjustment of the two parameters gain and variability threshold, a simple calculation of the global image statistics was performed and image noise was estimated. The median and variance of the grey values of all pixels were analyzed and saved as *median\_grey* and *var\_grey*, respectively. Before processing 'rgb' images, the parameter *median-grey* was calculated by averaging over the luminance values of all color channels. In contrast, *var\_grey* was calculated from the channel with the highest average luminance value. The parameter *gain* acts as a factor of ln-transformed grey values (adjusts image brightness) and was derived from *median\_grey* according to Eq. 1. *gain* cannot be smaller than 1.

$$\text{gain} = \mathbb{S} - median\\_grey\tag{1}$$

**39**

*From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising*

*N*=1 *X*

exhibiting various noise levels and brightness values.

**2.6 Image processing step 1: logarithmic transformation**

transformation was stored as *greyLn* at the focal pixel.

**2.7 Image processing step 2: adaptive spatial averaging**

10 pixels and the minimum size to 1 pixel.

*XNE* ∣ *mean*\_*grey*\_*local* < *var*\_*grey*/ (*median*\_*grey* − 1) (2)

The noise estimation of color images was restricted to the color channel with the

 *threshold*\_*var* = 0.0002 ∗ [(*noise*\_*estimate* + *var*\_*grey*) ∗ *median*\_*grey*] + 0.92 + ( *gain*/10) (4)

The logarithmic transformation of luminance values results in disproportionate

*greyLn* = ln (*grey*\_*value* + 1.5) ∗ *gain* (5)

Image noise was widely removed by means of 'adaptive spatial averaging', a procedure that is executed by each pixel and evaluates the local variability of the *greyLn* values to calculate the radius of the circle in which grey value averaging is executed (locals means computation). Adaptive spatial averaging was executed in parallel at the level of pixels. Each pixel expands a circle in steps of one patch as long as the variability of the average *greyLn* values of the pixels of each color channel within this circle remained below *threshold\_var*. Once the dimension of this circle was found, the *greyLn* values were averaged and saved as *grey\_avg* value for the focal pixel. This averaging of *greyLn* values in the circle was calculated for each color channel separately. The maximum radius of the expanding circle was restricted to

amplification of the small grey values. The image contrast of very dim images was improved by calculating the natural logarithm of the luminance values of patches of each color channel and multiplication of the result by the *gain* factor using Eq. (5). Adding the constant 1.5 to the luminance values prevents them from becoming smaller than zero after ln-transformation. The result of this logarithmic

If *noise\_estimate* was smaller than 0.01, *threshold\_var* was always set to 0.01, which is low enough to preserve fine image details, but high enough to remove the

This NV algorithm derives all its parameters from global image statistics (*median\_grey* and *var\_grey*) and the *noise\_estimate*. Eq. (4) defines the threshold that was used for the adaptive spatial averaging procedure. It was found empirically by testing numerous terms that allow to predict the parameters that result in high quality output images with respect to the conservation of image details and the (Peak signal-to-noise ratio) PSNR value. Eq. (4) was derived from manuallyadjusted parameter combinations that were obtained from 10 different images

*abs*(*mean*\_*grey*\_*local* − *luminance*)/*X* (3)

*DOI: http://dx.doi.org/10.5772/intechopen.91911*

*noise*\_*estimate* = ∑

highest average luminance value.

**2.5 Parameter estimation**

remaining noise.

## **2.4 Noise estimation**

A rather simple estimation of noise can be obtained by summing up the differences between the grey values of each pixel and the average grey values of surrounding pixels in rather homogeneous regions of the image. For this purpose, the local average luminance value (*mean\_grey\_local*) and local variance of luminance values (*var\_grey*) of surrounding patches were calculated in a circle with a radius of 4 pixels. Since brighter images tend to show a higher variability of grey values, the noise estimation was restricted to those pixels XNE having circles in which *mean\_ grey\_local* was smaller than the ratio between *var\_grey* and (*median\_grey* – 1) (see Eq. (2)). Noise estimation was computed according to Eq. (3), whereby X denotes the number of patches in the homogeneous image regions.

*From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising DOI: http://dx.doi.org/10.5772/intechopen.91911*

$$X\_{NE} \mid mean\\_grey\\_local < var\\_grey / (median\\_grey - 1)\tag{2}$$

$$\text{noise\\_estimate} = \sum\_{N=1}^{X} \text{abs}\{mean\\_grey\\_local\ - humanance\} / X \tag{3}$$

The noise estimation of color images was restricted to the color channel with the highest average luminance value.

#### **2.5 Parameter estimation**

*Biomimetics*

**2.2 Import pictures**

**2.3 Image statistics**

**2.4 Noise estimation**

is performed in the second image processing step of this night vision method and preserves object contours and image sharpness. This procedure assumes a higher variability of luminance values near object contours as compared to homogeneous image regions. Thus, circles in which local luminance values can be averaged may not exceed a critical variability of grey values (*threshold\_var*). Adaptive averaging is performed at the level of pixels and evaluates the variability of local grey values to find the dimension of a circle in which the variability of grey values remains below the predefined variability threshold. After exceeding this threshold, the average of grey values of pixels belonging to this circle is calculated and stored at the central pixel. As a final processing step an automatic contrast-enhancement procedure was applied by means of linear histogram stretching. Two parameters (gain and variability threshold) are essential for this method and were derived from global image statistics and a simple kind of noise estimate. The image-enhancement algorithm described here was developed using Netlogo 5.2 (developed by Uri Wilensky; http://ccl.northwestern.edu/ netlogo/), a multi-agent programming environment that allows the parallel execution

of commands at the level of pixels (named patches in the Netlogo language).

Dim and noisy images were imported into Netlogo using the command "importpcolors", which transformed the luminance values of pixels into the grey values of patches. This Netlogo function limits the luminance values of grey-scale images to the range between 0 (black) to 9.9 (white), whereby the total number of possible grey values is 110. Although image information is reduced by this function, the human eye is unable to recognize any difference between images having a grey value range of 110 or higher.

For the automatic adjustment of the two parameters gain and variability threshold, a simple calculation of the global image statistics was performed and image noise was estimated. The median and variance of the grey values of all pixels were analyzed and saved as *median\_grey* and *var\_grey*, respectively. Before processing 'rgb' images, the parameter *median-grey* was calculated by averaging over the luminance values of all color channels. In contrast, *var\_grey* was calculated from the channel with the highest average luminance value. The parameter *gain* acts as a factor of ln-transformed grey values (adjusts image brightness) and was derived

*gain* = 5 − *median*\_*grey* (1)

A rather simple estimation of noise can be obtained by summing up the differences between the grey values of each pixel and the average grey values of surrounding pixels in rather homogeneous regions of the image. For this purpose, the local average luminance value (*mean\_grey\_local*) and local variance of luminance values (*var\_grey*) of surrounding patches were calculated in a circle with a radius of 4 pixels. Since brighter images tend to show a higher variability of grey values, the noise estimation was restricted to those pixels XNE having circles in which *mean\_ grey\_local* was smaller than the ratio between *var\_grey* and (*median\_grey* – 1) (see Eq. (2)). Noise estimation was computed according to Eq. (3), whereby X denotes

from *median\_grey* according to Eq. 1. *gain* cannot be smaller than 1.

the number of patches in the homogeneous image regions.

**38**

This NV algorithm derives all its parameters from global image statistics (*median\_grey* and *var\_grey*) and the *noise\_estimate*. Eq. (4) defines the threshold that was used for the adaptive spatial averaging procedure. It was found empirically by testing numerous terms that allow to predict the parameters that result in high quality output images with respect to the conservation of image details and the (Peak signal-to-noise ratio) PSNR value. Eq. (4) was derived from manuallyadjusted parameter combinations that were obtained from 10 different images exhibiting various noise levels and brightness values.

$$\begin{array}{c} \text{threshold\\_var = 0.0002 \* [(noise\\_estimate + var\\_grey) \* median\\_grey]} \times 0.92 \\ \text{+ (gain/10)} \end{array} \tag{4}$$

If *noise\_estimate* was smaller than 0.01, *threshold\_var* was always set to 0.01, which is low enough to preserve fine image details, but high enough to remove the remaining noise.

#### **2.6 Image processing step 1: logarithmic transformation**

The logarithmic transformation of luminance values results in disproportionate amplification of the small grey values. The image contrast of very dim images was improved by calculating the natural logarithm of the luminance values of patches of each color channel and multiplication of the result by the *gain* factor using Eq. (5). Adding the constant 1.5 to the luminance values prevents them from becoming smaller than zero after ln-transformation. The result of this logarithmic transformation was stored as *greyLn* at the focal pixel.

$$\text{greyLn} = \ln \text{(grey\\_value + 1.5)} \ast \text{gain} \tag{5}$$

#### **2.7 Image processing step 2: adaptive spatial averaging**

Image noise was widely removed by means of 'adaptive spatial averaging', a procedure that is executed by each pixel and evaluates the local variability of the *greyLn* values to calculate the radius of the circle in which grey value averaging is executed (locals means computation). Adaptive spatial averaging was executed in parallel at the level of pixels. Each pixel expands a circle in steps of one patch as long as the variability of the average *greyLn* values of the pixels of each color channel within this circle remained below *threshold\_var*. Once the dimension of this circle was found, the *greyLn* values were averaged and saved as *grey\_avg* value for the focal pixel. This averaging of *greyLn* values in the circle was calculated for each color channel separately. The maximum radius of the expanding circle was restricted to 10 pixels and the minimum size to 1 pixel.

### **2.8 Image processing step 3: enhancement of image contrast**

After adaptive spatial averaging, image contrast can be enhanced if the grey value variability of the resulting image is low (variance <75). This was done by means of linear histogram stretching, which uses the lowest and highest *grey\_avg* values of the image to calculate the resulting grey value of each patch. This was achieved by using Eq. (6), which sets the lowest value to 0 (black) and the highest value to 9.9 (white) while intermediate values are assigned to shades of grey.

$$\begin{array}{c} \text{gry\\_value} = \{ \{ \text{gry\\_avg - min (gry\\_avg)} \} / \{ \text{max (gry\\_avg)} \} \\ \text{ -- min (gry\\_avg)} \} \* 9.9 \end{array} \tag{6}$$

#### **2.9 Evaluation of performance**

The performance of this algorithm was evaluated by calculating the peak signal-to-noise-ratio (PSNR) using the method described by Russo [20]. The result is given in dB and quantifies the difference between the noisy input image and the processed image. Higher dB values indicate better denoising performance. To evaluate PSNR between the input and the output images, images were exported from Netlogo in 'png' format and a Python script was programmed to execute the function "compare\_psnr" offered in the "skimage.measure" library.

### **3. Results image denoising**

The sequence of image-processing steps illustrated in **Figure 1** allowed the strong enhancement of the image contrast in dim images and simultaneously removed sensor noise. The performance of this simple, dynamic spatial domainfiltering algorithm depends on two parameters that can be estimated by evaluating global image statistics and executing a simple noise estimation method.

#### **Figure 2.**

*Performance of the NV algorithm using a dim image of a mobile phone camera as input. (A) The original image is black. (B) After automatic histogram equalization sensor noise was amplified. (C) The NV-processed image exhibits a high level of image contrast and contains a low amount of noise. PSNR of C: 25.9 dB. Hartbauer holds the copyright of this picture.*

**41**

**Figure 3.**

*From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising*

Application of this NV algorithm to an underexposed image that was taken in a very dim room resulted in an output image with a high level of contrast and rather low noise level (see **Figure 2C**). On the contrary, the automatic adjustment of brightness and contrast offered by commonly used image processing software

*Performance of the NV algorithm using a dim natural image as input. (A) Noisy image of a village on Mallorca (printed with permission from Buades; see Buades et al. [6]). (B) Noise was amplified after histogram stretching. (C) The output of the NV algorithm yielded a reduced amount of noise (PSNR = 35.3 dB)*

*and shows a higher quality regarding image details and contrast.*

*DOI: http://dx.doi.org/10.5772/intechopen.91911*

*From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising DOI: http://dx.doi.org/10.5772/intechopen.91911*

#### **Figure 3.**

*Biomimetics*

**2.8 Image processing step 3: enhancement of image contrast**

*grey*\_*value* = {[*grey*\_*avg* − *min*

− *min*

**2.9 Evaluation of performance**

**3. Results image denoising**

After adaptive spatial averaging, image contrast can be enhanced if the grey value variability of the resulting image is low (variance <75). This was done by means of linear histogram stretching, which uses the lowest and highest *grey\_avg* values of the image to calculate the resulting grey value of each patch. This was achieved by using Eq. (6), which sets the lowest value to 0 (black) and the highest value to 9.9 (white) while intermediate values are assigned to shades of grey.

The performance of this algorithm was evaluated by calculating the peak signal-to-noise-ratio (PSNR) using the method described by Russo [20]. The result is given in dB and quantifies the difference between the noisy input image and the processed image. Higher dB values indicate better denoising performance. To evaluate PSNR between the input and the output images, images were exported from Netlogo in 'png' format and a Python script was programmed to execute the

The sequence of image-processing steps illustrated in **Figure 1** allowed the strong enhancement of the image contrast in dim images and simultaneously removed sensor noise. The performance of this simple, dynamic spatial domainfiltering algorithm depends on two parameters that can be estimated by evaluating global image statistics and executing a simple noise estimation method.

*Performance of the NV algorithm using a dim image of a mobile phone camera as input. (A) The original image is black. (B) After automatic histogram equalization sensor noise was amplified. (C) The NV-processed image exhibits a high level of image contrast and contains a low amount of noise. PSNR of C:*

*25.9 dB. Hartbauer holds the copyright of this picture.*

function "compare\_psnr" offered in the "skimage.measure" library.

(*grey*\_*avg*)]/

[*max*

(*grey*\_*avg*)]} ∗ 9.9

(*grey*\_*avg*)

(6)

**40**

**Figure 2.**

*Performance of the NV algorithm using a dim natural image as input. (A) Noisy image of a village on Mallorca (printed with permission from Buades; see Buades et al. [6]). (B) Noise was amplified after histogram stretching. (C) The output of the NV algorithm yielded a reduced amount of noise (PSNR = 35.3 dB) and shows a higher quality regarding image details and contrast.*

Application of this NV algorithm to an underexposed image that was taken in a very dim room resulted in an output image with a high level of contrast and rather low noise level (see **Figure 2C**). On the contrary, the automatic adjustment of brightness and contrast offered by commonly used image processing software

either produced a rather dark image (similar as **Figure 2A**) or a contrasted image in which sensor noise was greatly enhanced (**Figure 2B**). The adaptive spatial averaging procedure preserved fine image details and object contours, while most noise was removed. This is mirrored in the high PSNR value of 25.9 dB when **Figure 2B** was compared with **Figure 2C**.

The NV algorithm enhanced image contrast and removed noise from the natural image showing a village (compare **Figure 3A** and **C**; PSNR = 35.3 dB), whereas automatic contrast enhancement amplified image noise greatly (**Figure 3B**). In comparison, denoising performed with an improved non local mean algorithm published by Buades et al. [6] removed noise slightly more effective (PSNR = 41.1 dB), but did not improve image contrast.

When Gaussian distributed noise with a standard deviation of 12.75 was added to an image of a bird (**Figure 4A**), the NV algorithm removed noise while retaining many fine image details (**Figure 4B**). This performance is reflected in a high PSNR value of 32.1 dB. The same noisy picture processed with a method that is based on the moving least squares (MLS) algorithm described by Lee et al. [9] resulted in the production of a rather blurry image.

The noisy grey-scale image, shown in **Figure 4C**, contains additive Gaussian noise with a standard deviation of 0.6. The output image of the NV algorithm

#### **Figure 4.**

*Denoising performance of the NV algorithm using grey scale images as input. (A) The image of a bird (from Lee et al. [9]) was degraded by Gaussian distributed noise with σ = 12.75. (B) The NV algorithm removed noise (PSNR = 32.1 dB) and preserved many fine image details. (C) Additive Gaussian noise with σ = 0.6 was added to an image of an alarm clock. The NV algorithm removed noise (PSNR = 27.4 dB) without degrading fine image details (D). The picture was printed with permission from Springer Inc. The picture shown in (C) was printed with permission from Yoon et al. [10] (Creative Commons License).*

**43**

**Figure 5.**

*From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising*

(**Figure 4D**) contains many fine image details and the noise level was reduced by 27.2 dB (PSNR). This denoising performance is very similar to the adaptive total variation minimization-based image enhancement method (ATVM) described by Yoon et al. [10], which is mirrored in a similar PSNR value of

Adaptive spatial averaging as described here can also be used to remove noise from color images (see **Figure 5**). This NV algorithm successfully removed noise from the extremely noisy image showing a candle (**Figure 5A**). Denoising of this image resulted in a PSNR of 32.0 dB (**Figure 5B**), which is similar to the denoising performance of the GSM wavelet denoising described by Portilla et al. [23]. The NV algorithm also removed most noise from the image showing the face of an ostrich in **Figure 5C**, resulting in a PSNR value of 32.7 dB. Interestingly, the resulting image is rather sharp and still contains many fine image details, such as the hairs. In contrast, the novel image denoising method described by Liu et al. [4] created

*The NV algorithm reduced the noise level in extremely noisy color images. (A) Noisy color image of a camera taken at candle light conditions. The NV algorithm removed most noise (PSNR = 32.0 dB) (B). (C) Noisy color image of an ostrich with 10% additive Gaussian noise. (D) Noise reduced output of the NV algorithm*

*(PSNR = 32.7 dB). Pictures printed with permission from IEEE (see Liu et al. [4]).*

*DOI: http://dx.doi.org/10.5772/intechopen.91911*

30.5 dB.

### *From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising DOI: http://dx.doi.org/10.5772/intechopen.91911*

*Biomimetics*

**Figure 2B** was compared with **Figure 2C**.

but did not improve image contrast.

production of a rather blurry image.

**42**

**Figure 4.**

*Denoising performance of the NV algorithm using grey scale images as input. (A) The image of a bird (from Lee et al. [9]) was degraded by Gaussian distributed noise with σ = 12.75. (B) The NV algorithm removed noise (PSNR = 32.1 dB) and preserved many fine image details. (C) Additive Gaussian noise with σ = 0.6 was added to an image of an alarm clock. The NV algorithm removed noise (PSNR = 27.4 dB) without degrading fine image details (D). The picture was printed with permission from Springer Inc. The picture shown in (C)*

either produced a rather dark image (similar as **Figure 2A**) or a contrasted image in which sensor noise was greatly enhanced (**Figure 2B**). The adaptive spatial averaging procedure preserved fine image details and object contours, while most noise was removed. This is mirrored in the high PSNR value of 25.9 dB when

The NV algorithm enhanced image contrast and removed noise from the natural

When Gaussian distributed noise with a standard deviation of 12.75 was added to an image of a bird (**Figure 4A**), the NV algorithm removed noise while retaining many fine image details (**Figure 4B**). This performance is reflected in a high PSNR value of 32.1 dB. The same noisy picture processed with a method that is based on the moving least squares (MLS) algorithm described by Lee et al. [9] resulted in the

The noisy grey-scale image, shown in **Figure 4C**, contains additive Gaussian noise with a standard deviation of 0.6. The output image of the NV algorithm

image showing a village (compare **Figure 3A** and **C**; PSNR = 35.3 dB), whereas automatic contrast enhancement amplified image noise greatly (**Figure 3B**). In comparison, denoising performed with an improved non local mean algorithm published by Buades et al. [6] removed noise slightly more effective (PSNR = 41.1 dB),

*was printed with permission from Yoon et al. [10] (Creative Commons License).*

(**Figure 4D**) contains many fine image details and the noise level was reduced by 27.2 dB (PSNR). This denoising performance is very similar to the adaptive total variation minimization-based image enhancement method (ATVM) described by Yoon et al. [10], which is mirrored in a similar PSNR value of 30.5 dB.

Adaptive spatial averaging as described here can also be used to remove noise from color images (see **Figure 5**). This NV algorithm successfully removed noise from the extremely noisy image showing a candle (**Figure 5A**). Denoising of this image resulted in a PSNR of 32.0 dB (**Figure 5B**), which is similar to the denoising performance of the GSM wavelet denoising described by Portilla et al. [23]. The NV algorithm also removed most noise from the image showing the face of an ostrich in **Figure 5C**, resulting in a PSNR value of 32.7 dB. Interestingly, the resulting image is rather sharp and still contains many fine image details, such as the hairs. In contrast, the novel image denoising method described by Liu et al. [4] created

#### **Figure 5.**

*The NV algorithm reduced the noise level in extremely noisy color images. (A) Noisy color image of a camera taken at candle light conditions. The NV algorithm removed most noise (PSNR = 32.0 dB) (B). (C) Noisy color image of an ostrich with 10% additive Gaussian noise. (D) Noise reduced output of the NV algorithm (PSNR = 32.7 dB). Pictures printed with permission from IEEE (see Liu et al. [4]).*

patchy regions of homogeneous color, when the same noisy image was used as input. Furthermore, GSM wavelet denoising of this image resulted in image blur.


**2. Background**

#### *2.1. Cost Estimation for EES Systems*

The estimation of the total cost (and the possible resulting economic benefit) of an EES should consider a number of cost items; namely, the initial investment, the runtime operation expenditures (i.e., maintenance and obsolescence), and the cost of energy consumption, which depends on the overall architecture and the load profiles. Once a comprehensive model is available, it will allow one to compare different solutions (mainly the allocation of the energy flow, and the choice and sizing of the components), so as to choose the most profitable one, by taking into account a number of constraints and of optimization goals.

Given its relevance in many domains (industry, residential, large-scale energy generation installations), the literature on the topic is quite rich. The most recent solutions proposed at state of the art are summarized in Table A1 (moved to the Appendix A not to interrupt the natural flow of the paper). The Table highlights the goal of each work, how EES components are modeled, the considered costs, and most importantly, the solution proposed by each work, which mostly fall under two categories: optimization-based approaches and simulation-based approaches.

*Optimization-based solutions* use analytical or empirical equation-based models of the power characteristics of EES components and the corresponding costs, and formulate the problem into the constrained optimization of a given target; e.g., maximization of power production, or minimization of a cost function [13–31]. Unfortunately such approaches suffer from many limitations. Given that the focus is optimization of some economic parameter, the evolution of EES components is considered only as a byproduct: either as a constraint or an optimization goal [29–31], or as a dimension of the problem that can be reproduced with simplified models or even simple input traces [13–18,24,25,28,32], thereby sacrificing accuracy to simplify computation. When accurate models are adopted, they are restricted to a subset of the components considered of interest (e.g., only batteries or PV modules), while the other EES components are either ignored or modeled with simplistic equations [20–23,26,27]. Few works take into account the power management strategy used to activate power sources and energy storage devices. When this happens, the goal is determining the optimal day-ahead scheduling of energy storage devices, thereby preventing the comparison of different power management strategies [26–31]. As a result, power dynamics are always considered as a minor dimension of the problem (i.e., a constraint, an optimization goal, or an input), and the mutual impact of power and cost is completely lost. Finally, these approaches provide one solution (or a few) and do not easily allow a comparative analysis of various solutions, nor the sensitivity of a solution with respect to some of the parameters.

*Simulation-based approaches* optimize EES design from an economic perspective based on dynamic simulations of alternative configurations [33–35]. The goal is the evaluation of the impact of electricity pricing, the feasibility of the constructed EES, or the evaluation of the most economical alternative. However, once again the focus is on the economic dimension, thereby adopting gross grain temporal scales (e.g., 1 h time steps) and restricting accurate modeling only to specific classes of EES components of interest (e.g., PV modules [33,34]). Finally, the focus is restricted to only one direction of the mutual impact between the cost dimension and power dynamics of the EES under analysis, which are evaluated strictly sequentially, depending on the causality of interest.

The main limitations of the aforementioned approaches are thus the adoption of abstract and simplistic models of EES components and the gross grain temporal scale: the energy dimension is thus modeled with a very low level of detail, thereby missing important intrinsic dynamics of the EES components, together with the modeling of inefficiencies and of realistic operating conditions. Additionally, cost and power evolution are never considered as mutually interacting dimensions, thereby missing important dimensions of the analysis.

The work proposed in this paper can be viewed as a different perspective; while it is a simulation-based approach, it *leverages results of an independent simulation of power traces*, which can use models with variable accuracy and possibly with different time scales; these power traces are then fed into the various models of different cost items (where they are power-dependent), which can vice versa influence and impact the power dimension. This kind of modular approach is not, however, the result of two distinct simulation environments, but both power and cost information can be derived by concurrently simulating them: this allows one to expose all mutual dependencies between power and cost, and thus to improve the design of EES with a more informed view of all the variables. There is one study [36] that focused on the modeling the different components in the EES by using a different model of computation (MoC) in one simulation framework by using SystemC-AMS. Cost is indeed mentioned in that study, but there is no indication of how the cost models of that work are linked to the power simulation. These are simply obtained by a post-processing of the power traces, as done in most related works. Our proposed simulation framework speeds up the design-time optimization process and has different methods to evaluate the power flow and economic benefit of EES by concurrently simulating the two quantities. The key for such a modular and concurrent simulation approach is described in the following section.

#### *2.2. A Multi-Layered Approach for Functional and Extra-Functional Simulation*

The simultaneous simulation of the various aspects of a system requires the construction of frameworks that integrate different views of a system in a single run. The work in [9], which targeted smart electronic systems, proposed an effective methodology that allows the simulation of system functionality together with its power, thermal, and reliability evolution.

The approach proposed in [9] envisions a multi-layer, bus-centric framework (depicted on the left-hand side of Figure 1, adapted from the original paper): *multi-layer* because it has a stacked layers structure; each layer is associated with one simulated characteristic of the system (called *property*). *Bus-centric* means each property is simulated with a virtual bus in each layer, used to update the property-specific status of the system. All simulated properties are thus reduced to this common structure, thereby easing synchronization and information exchange. The goal is indeed to avoid the construction of co-simulation frameworks, and to rather simulate all layers in a single run by falling on the same implementation language; namely, SystemC-AMS.

Each layer is fixed to a single generic underlying *architecture*, of which the layer-specific bus is the central element, used to carry information between components (*layer-specific signals*). Such information is specific of the property under analysis in the layer (e.g., voltage and current for the power layer). Each component in the system corresponds to a model in each layer, to capture the property-specific evolution over time of the component. The *layer-specific bus* aggregates the property-specific information of each component to control the information flow and update the overall property-specific information. Layers can additionally share information (*inter-layer signals*) to mutually influence each other. Notice that signals can go in both directions; i.e., they can be forwarded to upper layers, as signals that are needed to carry out the simulation at that layer (solid lines); but signals can also be fed back to lower layers as information that can make the lower-level simulation more accurate (dotted lines). In the context of [9], for example, the former type of signals could be power consumption signals forwarded upwards to compute the temperature map of the system; the latter type could be the same temperature maps, which could impact the power consumption of some of the components. Most importantly, when the layers run at different time scales, *time converters* can be introduced to convert the signals towards coarser or finer time scales. For instance, while it makes sense to measure the functionality of an electronic system at the nanosecond scale, this is not a required granularity for power simulation, and can be even more relaxed for the analysis of the thermal flow.

Not shown in the figure, there are also *layer-specific description data* that are strictly referring to that layer and are needed as a sort of "context" of the simulation. In the temperature layer of [9], these data could refer either to the physical locations of the components, which affect the thermal flow, or to the characteristics of the materials (type, thickness, etc.) constituting the system itself.

**Figure 1.** The multiple layered framework for the non-functional properties simulation proposed in [9] (**left**) and application to the simulation of power and cost proposed in this paper (**right**).

This paradigm proved to work very well for the simulation of the different views of a smart system, thanks to its scalability and to the simultaneous evolution of the different system properties, which thus can react simultaneously the one to changes of the other.

For this reason, in this work we tried to fit the above-described paradigm to the *co-simulation of power and cost of an EES*, with the objective of exploiting its two main benefits; namely, modularity and generality. Notice that the contribution of this work is showing that the layered, modular framework of [9] is not a customized architecture, but its paradigm can be extended to other quantities: in this specific case the operational costs of an EES. The work of [9] was applied to small-scale electronic systems, where the non-functional properties were (from bottom to top; see Figure 1 in the manuscript) power, temperature, and reliability. Our work aims at demonstrating the general applicability of the modeling and simulation paradigm of [9] to the context of large-scale EES, and can add different non-functional properties that the properties in this work are power and cost. The right-hand side of Figure 1 shows how the generic bus-based layered architecture maps to our specific context. Since we focus on EES, the first layer of the simulation stack coincides with the power layer. In some sense, this represents the equivalent of "functionality" in the original version; that is, the lowest abstraction level of the semantics. On top of the power layer sits the cost layer, which receives power flow information (used to update the power-dependent cost items, such as electricity cost) and returns cost information to the power layer, which can be used to implement policies in the power bus to decide the optimal power flow among the various components.

Time converters are also expected, since some updates of some of the cost items might generally have different time granularity with respect to power updates, which in the finest granularity are updated every 15 min (as in the most accurate meters).

In the conceptual architecture of Figure 1, the power layer is fundamentally working just as described in [9]; even if components are different in their power scale (100 s of Watts vs. milliwatts), their interfaces and interaction are the same. The cost layer, conversely, is an original layer and its implementation within the constraints of the layered structure demonstrates the claim of modularity of the architecture. In the next section, we will describe the cost models and the technical details to incorporate the cost layer within the template of Figure 1.


*5.1.3 Data analysis of oilseeds production in SSA*

1.Soybean

Soybean is a vital crop for at least one million African smallholder farmers. Other factors have contributed to rising soybean demand, such as the need for domestic processing to meet rising domestic demand for soybean meal,

*A Big Data Analytics Architecture Framework for the Production and International Trade… DOI: http://dx.doi.org/10.5772/intechopen.107225*

#### **Figure 8.** *Soybean world production and yield/ha.*

especially for the poultry feed industry, and the favorable outlook for edible oil. The United States, Brazil, and Argentina, as the world's top three soybean exporters, will continue to account for approximately 90% of global soybean, soybean meal, and soybean oil exports in the coming decade. The soybean production levels and yield per hectare for the world, Sub-Saharan Africa (SSA) and other countries are shown on **Figure 8** below. Both area expansion and yield growth have contributed about equal amounts to the reported growth in soybean output in SSA, with yearly growth rates of 3% in area and 3.5 percent in yield.