**1. Introduction**

The inevitable depletion of the world's petroleum supply and the increasing problem of greenhouse gas effects have resulted in an increasing worldwide interest in alternative nonpetroleum-base source of energy. As the transportation sector is practically entirely depending on oil and as it is responsible for half of the total CO2 emission [1], the in‐ creasing in market share of renewable biofuels includes ethanol fuel. The uses of ethanol fuel will significantly reduce net carbon dioxide emission once it replaces fossil fuels be‐ cause fermentation-derived ethanol is already a part of the global carbon cycle. Howev‐ er, to enhance the market position of the biofuel the production cost should be reduced. Nowadays, the raw material and enzyme production are the two main contributors to the overall costs, thus using high cellulose containing agricultural residues as feedstock agricultural could result in cost reduction. The techniques employed to produce bioetha‐ nol from agricultural residue materials or lignocellulosic materials are subjected to the same economical demands as the more traditional sugar and starch processes, as the price of bioethanol must be competitive with that of petrol. Conversion of lignocellulosic materials to monomeric sugars and finally ethanol must thus be performed at low cost, while still achieving high yields. This can be done by developing processes that require limited amounts of the material chemicals, yeast and enzymes. To convert lignocellulosic materials to monomeric sugars, they must pretreat by different methods, such as dilute acid, steam explosion, ammonia fiber explosion (AFEX) and dilute alkali. All of these methods can change lignocellulosic structure and enhance the enzymatic saccharification of cellulose to hexose sugar.

© 2013 Punsuvon; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Punsuvon; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The bioconversion process from lignocellulose biomass to ethanol consists basically of three steps: pre-treatment, enzymatic hydrolysis and fermentation. Pretreatment is a necessary step to facilitate the enzymatic attack of lignocellulosic materials. Steam explosion is recognized as an efficient pre-treatment method in ethanol production [2]. The raw material is treated at high pressure steam followed by suddenly rapid reduction in pressure resulting in substantial breakdown of the lignocellulosic structure, hydrolysis of the hemicellulosic fraction, depoly‐ merization of the lignin components and defibration [3]. Therefore, the accessibility of the cellulose components to degrade by enzymes is greatly increased. The process of ethanol production from lignocellulosic material is shown in Figure 1.

ment was proposed. Hemicellulose was hydrolyzed by dilute H2SO4, allowing a substantial recovery of xylose. Lignin was removed by means of a mild alkaline/oxidative solubilization procedure, involving no sulphite or chlorine and its derivatives. The use of diluted reagents and relatively low temperatures was both cheap and environmentally friendly. The pretreated material was nearly pure cellulose, whose enzyme hydrolysis proceeded fast with high yields, that leading to high glucose syrup of remarkable purity. Ballesteros *et al.* [8] investigated the enzyme hydrolysis of steam exploded herbaceous agricultural waste (*Brassica carinata*) at different particle sizes. The objective of this work was to evaluate the effect of particle size on steam explosion pretreatment of herbaceous lignocellulosic biomass. Hemicellulose and cellulose recovery and effectiveness of enzyme hydrolysis of the cellulosic residue was presented for the steam-exploded agricultural residue (*Brassica carinata*) with different particle sizes. The parameters tested were: particle size (2-5, 5-8 and 8-12 mm), temperature (190 and

Optimization of Delignification and Enzyme Hydrolysis of Steam Exploded Oil Palm Trunk for Ethanol Production ...

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159

C), and residence time (4 and 8 min). The composition analysis of filtrate and water insoluble fibre after pretreatment and enzyme digestibility data were presented. The results showed that larger steam exploded particle (8-12 mm) resulted in higher cellulose and enzyme digestibility. The use of small particles in steam explosion would not be desirable in optimizing the effectiveness of the process improving economy. Punsuvon *et al*. [9] studied the fractiona‐ tion of chemical components of oil palm trunk by steam explosion. The results showed optimal conditions for pretreatment at temperature 214°C for 2 min of steam explosion. Ohgren *et al*. [10] reported the ethanol fuel production from steam-pretreated corn stover using SSF at higher dry matter content. This study was performed on steam-pretreated corn stover at 5, 7.5 and 10% water-insoluble solids (WIS) with 2 g/L hexose fermenting *Saccharomyces cerevisiae*. The results showed that SSF at 10% WIS gave 74% of ethanol yield based on the glucose content in the raw material. Ruiz *et al*. [11] studied the steam explosion pretreatment prior to enzymatic hydrolysis of sunflower stalks. The stalks were subjected to steam explosion pretreatment in the temperatures ranging between 180ºC and 230ºC. The steam-exploded pulp was further hydrolyzed by enzyme. The result showed that after 96 hours of enzymatic reaction, a maximum hydrolysis yield of 72% was obtained after pretreatment at 220ºC, corresponding to a glucose concentration of 43.7 g/L in hydrolysis media. With regard to the filtrate analysis, most of the hemicellulosic derived sugars released during the steam pre-treatment were in the oligomeric form. The highest recovery was obtained at 210ºC of pretreatment temperature. Moreover, the utilization of hemicellulosic-derived sugars as a fermentation substrate would

improve the overall bioconversion of sunflower stalks into ethanol fuel.

Response surface methodology is an empirical statistical technique employed for multiple regressions analysis by using quantitative data. It solves multivariable data which is obtained from properly designed experiments to solve multivariable equation simultaneously. The graphical representation of their function was called response surface, which is used to describe the individual and cumulative effect of the test variables and their subsequent effect on the response. The effect of the variables on the response is investigated using second-order polynomial regression equation. This equation, derived using RSM for the evaluation of the

**1.1. Response Surface Methodology (RSM)**

response variables, is as follows:

210º

**Figure 1.** Flowchart of procedure for ethanol production

Numerous experimental studies of ethanol production from biomass have been carried out. A review article by Kaur *et al*. [4] examined the steam explosion of sugarcane bagasse as a pretreatment for ethanol production. In order to identify the optimum conditions of steam explosion, a range of operating temperatures at 188 – 243ºC and residence times at 0.5 - 44 min were applied. The results showed that pretreatment with steam explosion followed by enzyme hydrolysis had high efficiency in converting monosaccharide sugar to ethanol. Nunes *et al.* [5] reported the steam explosion pretreatment and enzymatic hydrolysis of Eucalyptus wood. The comparison under conditions of acid and non acid impregnation of wood before steam explosion was experimented. The results demonstrated the same solubilization effect of both experiments. Ballesteros *et al*. [6] reported that simultaneous saccharification and fermentation (SSF) process for ethanol production from various lignocellulosic woody (poplar and euca‐ lyptus) and herbaceous (*Sorghum* sp. bagasse, wheat straw and *Brassica carinata* residue) materials had been assayed using the thermotolerant yeast strain. Biomass samples were previously treated in a steam explosion pilot plant to provide biomass with increased cellulose content relative to untreated materials and to enhance cellulase accessibility. SSF experiments were performed in laboratory conditions at 42ºC for 160 hours. The results showed that eucalyptus, wheat straw and sweet sorghum bagasse gave ethanol concentration at 17, 18 and 16.0 g/L respectively, in 72 hours of fermentation. Montane *et a*l. [7] studied the steam explosion of wheat straw. A fractionation of wheat straw components in a two-step chemical pretreat‐ ment was proposed. Hemicellulose was hydrolyzed by dilute H2SO4, allowing a substantial recovery of xylose. Lignin was removed by means of a mild alkaline/oxidative solubilization procedure, involving no sulphite or chlorine and its derivatives. The use of diluted reagents and relatively low temperatures was both cheap and environmentally friendly. The pretreated material was nearly pure cellulose, whose enzyme hydrolysis proceeded fast with high yields, that leading to high glucose syrup of remarkable purity. Ballesteros *et al.* [8] investigated the enzyme hydrolysis of steam exploded herbaceous agricultural waste (*Brassica carinata*) at different particle sizes. The objective of this work was to evaluate the effect of particle size on steam explosion pretreatment of herbaceous lignocellulosic biomass. Hemicellulose and cellulose recovery and effectiveness of enzyme hydrolysis of the cellulosic residue was presented for the steam-exploded agricultural residue (*Brassica carinata*) with different particle sizes. The parameters tested were: particle size (2-5, 5-8 and 8-12 mm), temperature (190 and 210º C), and residence time (4 and 8 min). The composition analysis of filtrate and water insoluble fibre after pretreatment and enzyme digestibility data were presented. The results showed that larger steam exploded particle (8-12 mm) resulted in higher cellulose and enzyme digestibility. The use of small particles in steam explosion would not be desirable in optimizing the effectiveness of the process improving economy. Punsuvon *et al*. [9] studied the fractiona‐ tion of chemical components of oil palm trunk by steam explosion. The results showed optimal conditions for pretreatment at temperature 214°C for 2 min of steam explosion. Ohgren *et al*. [10] reported the ethanol fuel production from steam-pretreated corn stover using SSF at higher dry matter content. This study was performed on steam-pretreated corn stover at 5, 7.5 and 10% water-insoluble solids (WIS) with 2 g/L hexose fermenting *Saccharomyces cerevisiae*. The results showed that SSF at 10% WIS gave 74% of ethanol yield based on the glucose content in the raw material. Ruiz *et al*. [11] studied the steam explosion pretreatment prior to enzymatic hydrolysis of sunflower stalks. The stalks were subjected to steam explosion pretreatment in the temperatures ranging between 180ºC and 230ºC. The steam-exploded pulp was further hydrolyzed by enzyme. The result showed that after 96 hours of enzymatic reaction, a maximum hydrolysis yield of 72% was obtained after pretreatment at 220ºC, corresponding to a glucose concentration of 43.7 g/L in hydrolysis media. With regard to the filtrate analysis, most of the hemicellulosic derived sugars released during the steam pre-treatment were in the oligomeric form. The highest recovery was obtained at 210ºC of pretreatment temperature. Moreover, the utilization of hemicellulosic-derived sugars as a fermentation substrate would improve the overall bioconversion of sunflower stalks into ethanol fuel.

#### **1.1. Response Surface Methodology (RSM)**

The bioconversion process from lignocellulose biomass to ethanol consists basically of three steps: pre-treatment, enzymatic hydrolysis and fermentation. Pretreatment is a necessary step to facilitate the enzymatic attack of lignocellulosic materials. Steam explosion is recognized as an efficient pre-treatment method in ethanol production [2]. The raw material is treated at high pressure steam followed by suddenly rapid reduction in pressure resulting in substantial breakdown of the lignocellulosic structure, hydrolysis of the hemicellulosic fraction, depoly‐ merization of the lignin components and defibration [3]. Therefore, the accessibility of the cellulose components to degrade by enzymes is greatly increased. The process of ethanol

158 Sustainable Degradation of Lignocellulosic Biomass - Techniques, Applications and Commercialization

Numerous experimental studies of ethanol production from biomass have been carried out. A review article by Kaur *et al*. [4] examined the steam explosion of sugarcane bagasse as a pretreatment for ethanol production. In order to identify the optimum conditions of steam explosion, a range of operating temperatures at 188 – 243ºC and residence times at 0.5 - 44 min were applied. The results showed that pretreatment with steam explosion followed by enzyme hydrolysis had high efficiency in converting monosaccharide sugar to ethanol. Nunes *et al.* [5] reported the steam explosion pretreatment and enzymatic hydrolysis of Eucalyptus wood. The comparison under conditions of acid and non acid impregnation of wood before steam explosion was experimented. The results demonstrated the same solubilization effect of both experiments. Ballesteros *et al*. [6] reported that simultaneous saccharification and fermentation (SSF) process for ethanol production from various lignocellulosic woody (poplar and euca‐ lyptus) and herbaceous (*Sorghum* sp. bagasse, wheat straw and *Brassica carinata* residue) materials had been assayed using the thermotolerant yeast strain. Biomass samples were previously treated in a steam explosion pilot plant to provide biomass with increased cellulose content relative to untreated materials and to enhance cellulase accessibility. SSF experiments were performed in laboratory conditions at 42ºC for 160 hours. The results showed that eucalyptus, wheat straw and sweet sorghum bagasse gave ethanol concentration at 17, 18 and 16.0 g/L respectively, in 72 hours of fermentation. Montane *et a*l. [7] studied the steam explosion of wheat straw. A fractionation of wheat straw components in a two-step chemical pretreat‐

Fermentation by yeast Ethanol

Pulp

Delignification by alkaline

production from lignocellulosic material is shown in Figure 1.

Enzyme hydrolysis Delignified pulp

Glucose

**Figure 1.** Flowchart of procedure for ethanol production

Lignocelluloses materials Steam explosion pretreatment

Response surface methodology is an empirical statistical technique employed for multiple regressions analysis by using quantitative data. It solves multivariable data which is obtained from properly designed experiments to solve multivariable equation simultaneously. The graphical representation of their function was called response surface, which is used to describe the individual and cumulative effect of the test variables and their subsequent effect on the response. The effect of the variables on the response is investigated using second-order polynomial regression equation. This equation, derived using RSM for the evaluation of the response variables, is as follows:

$$\mathbf{Y} = \mathbf{b}\_{\mathbf{o}} + \sum\_{i=1}^{4} \mathbf{b}\_{i} \mathbf{x}\_{i} + \sum\_{i=1}^{4} \mathbf{b}\_{ii} \mathbf{x}\_{i}^{2} + \sum\_{i$$

Where Y is the response, bo, b, bii and bij are regression coefficients for intercept, linear, quadratic and interaction terms, respectively. The xi and xij are uncoded values for independent variables. An analysis of variance (ANOVA) is performed to determine the lack of fit and the effect of linear, quadratic and interaction terms on the response. Many researches have used RSM in optimization process as these examples. Roberto *et al*. [12] studied the dilute acid hydrolysis to recover xylose from rice straw in a semi-pilot reactor. Rice straw is consisted of pentose that could be used as a raw material for the production of many useful compounds. One of these was xylitol, with a potential application in the food and medical areas. The interest in biotech‐ nological processes employing lignocellulosic residues was increased because this material was cheap, renewable and widespread sugar sources. The objective of the study was to determine the effects of H2SO4 concentration and reaction time on the production of sugars (xylose, glucose and arabinose) and on the reaction byproducts (furfural, HMF and acetic acid). Dilute sulfuric acid was used as a catalyst for the hydrolysis of rice straw at 121ºC in a 350-L batch hydrolysis reactor. Rationale for conducting this study was determined based on a central composite statistical design. Response surface methodology (RSM) was adopted to optimize the hydrolysis conditions aiming to attain high xylose selectivity. The optimum condition was 1% H2SO4 concentration for 27 min. This condition gave 77% of xylose yield and 5.0 g/g of selectivity. Kunamneni *et al*. [13] applied the response surface to optimize the enzymatic hydrolysis of maize starch for higher glucose production. Doses of pre-cooked αamylase, post-cooked α-amylase, glucoamylase and saccharification temperature were examined to produce maximum conversion efficiency and all values were selected for optimization. Full factorial composite experimental design and response surface methodology were used in the experiment design and result analysis. The optimum values for the tested variables were: 2.243 U of pre-cooked α-amylase /mg solids, 3.383 U of post-cooked 3.383 U of α-amylase /mg solids, 2.243 U of glucoamylase /mg solids at a saccharification temperature of 55.1ºC. The maximum conversion efficiency of 96.25% was achieved. This method was efficient because only 28 experiments were necessary for the assessment and also the model adequacy was very satisfactory.

palm trunk every year can create massive pollutions thus development technology for valueadded products are need for this raw material. There are many need uses of potential valueadded products made from oil palm trunk such as particleboard, laminated board, plywood, fiberboard and furniture [16]. Oil palm trunk can also be used for making paper [17]. It can also

Optimization of Delignification and Enzyme Hydrolysis of Steam Exploded Oil Palm Trunk for Ethanol Production ...

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The objectives of this research are performed according to central composite design (CCD) and response surface methodology (RSM) to optimize and compare the condition for delignifica‐ tion and hydrolysis of steam-exploded oil palm trunk prior to ethanol fermentation to understand the relationship between the critical factor involved in enzymatic degradation of

The steam-exploded pulp obtained from oil palm trunk was prepared by steam explosion treatment. An amount of 150 g of dry oil palm trunk chip sample was placed in 2.5 L batch digester (Nitto Koatsu Company, Japan). Heating was accomplished by direct steam injection into the digester and the temperature of steam at 214°C for 2 min. This condition was previous work by Punsuvon *et al.* [9]. It could briefly explained that oil palm trunk chip was steamed at temperatures varying between 214 and 220ºC for 2 and 5 minutes. The optimization of the pretreatment condition was 214ºC and 2 minutes that gave the highest glucose yield after enzyme hydrolysis. In this studied, the explosive discharge of the digester contents into a collecting tank was actuated by rapidly opening a value. The combined pulp slurry was collected and washed with hot water (80°C) at total volume of 2 L for 30 min. The pulp was filtered and dried at room temperature for using as raw material in alcohol production study.

Saccharomyces cervisiae TIRS 5339 obtained from TISTR, Thailand was used in this study. It was maintained on a medium containing 20.0 g/l glucose, 20.0 g/l peptone and 10.0 g/l yeast extract at 4ºC and subcultured every month at 30ºC. The growth medium of the yeast consisted of 10.0 g/l yeast extract, 6.4 g/l urea, 2.0 g/l KH2PO4, 1.0 g/l MgSO4-7H2O and 2.0 g/l glucose at

The water-insoluble cellulose pulp obtained from steam explosion was delignified with potassium hydroxide. The reactions were carried out in a beaker under various maintained temperature. Before RSM was applied on alkaline delignification, approximate conditions for glucose content in pulps, namely concentration of pulp, concentration of alkaline solution, reaction time and temperature were determined by varying one factor at time while keeping the other constant. The initial step of the preliminary experiment was to select an appropriate amount of concentration of pulp. Five different concentrations of pulp (3, 6, 9, 12, 15 %w/v) were examined. The other three factors, concentration of alkaline solution, reaction time and

be used as raw material in ethanol production, too [9].

pulp and conversion to ethanol.

**2. Materials and methods**

pH 5.5 [18].

**2.1. Raw material and microorganism**

**2.2. Alkaline delignification of steam-exploded pulp**

#### **1.2. Oil palm trunk**

The oil palm tree (Elaeis guineensis) is indigenous to the tropical forests in weat Africa. The oil palm tree has become one of the most valuable commercial cash a crop due to the palm oil is used as a raw material in many industries such as soap, cosmetic, detergent, vegeTable oil and biodiesel. Nowaday almost 80% of the world oil palm plantation is centered at Southeast Asia, with most of it occurring in Indonesia (5.44 x 106 hectares) and Malaysia (4.85 x 106 hectares). Ad‐ ditionally, there are 260,000 hectares planted in Thailand, with smaller areas in the Philippines and some recent planting in Cambodia and Myanmar [14]. Oil palm trunks are available only when the economic lifespan of the palm is reach at the time of replanting. The average age of re‐ planting is approximately 25 years. The main economic criteria for felling are the height of the palm, reaching 13 m or above and the diameter of the felled trunk is around 45 cm to 65 cm. More than 15 million tons of oil palm trunks per year are replanted in the world [15]. The increase of oil palm trunk every year can create massive pollutions thus development technology for valueadded products are need for this raw material. There are many need uses of potential valueadded products made from oil palm trunk such as particleboard, laminated board, plywood, fiberboard and furniture [16]. Oil palm trunk can also be used for making paper [17]. It can also be used as raw material in ethanol production, too [9].

The objectives of this research are performed according to central composite design (CCD) and response surface methodology (RSM) to optimize and compare the condition for delignifica‐ tion and hydrolysis of steam-exploded oil palm trunk prior to ethanol fermentation to understand the relationship between the critical factor involved in enzymatic degradation of pulp and conversion to ethanol.


*Article* **Sorbicillinoid Derivatives with the Radical Scavenging Activities from the Marine-Derived Fungus** *Acremonium chrysogenum* **C10**

**Chengbao Duan 1,2,†, Shiyuan Wang 1,2,†, Ruiyun Huo 1,2,†, Erwei Li 3 , Min Wang 4 , Jinwei Ren 1 , Yuanyuan Pan 1,\*, Ling Liu 1,2,\* and Gang Liu 1,2,\***

**Abstract:** Sorbicillinoids are a class of structurally diverse hexaketide metabolites with good biological activities. To explore new structural sorbicillinoids and their bioactivities, the marine-derived fungus *Acremonium chrysogenum* C10 was studied. Three new sorbicillinoid derivatives, acresorbicillinols A–C (**1**–**3**), along with five known ones, trichotetronine (**4**), trichodimerol (**5**), demethyltrichodimerol (**6**), trichopyrone (**7**) and oxosorbicillinol (**8**), were isolated. The structures of new sorbicillinoids were elucidated by analysis of nuclear magnetic resonance (NMR) and high-resolution electrospray ionization mass spectroscopy (HRESIMS). The absolute configurations of compounds **1**–**3** were determined by comparison of the experimental and calculated electronic circular dichroism (ECD) spectra. Compound **3** exhibited a strong 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity, with the IC50 value ranging from 11.53 ± 1.53 to 60.29 ± 6.28 µM in 24 h. Additionally, compounds **2** and **3** showed moderate activities against *Staphylococcus aureus* and *Cryptococcus neoformans*, with IC50 values of 86.93 ± 1.72 and 69.06 ± 10.50 µM, respectively. The boundary of sorbicillinoid biosynthetic gene cluster in *A. chrysogenum* was confirmed by transcriptional analysis, and the biosynthetic pathway of compounds **1**–**8** was also proposed. In summary, our results indicated that *A. chrysogenum* is an important reservoir of sorbicillinoid derivatives, and compound **3** has the potential for new natural agents in DPPH radical scavenging.

**Keywords:** sorbicillinoids; *Acremonium chrysogenum*; structure elucidation; DPPH radical scavenging activity; antimicrobial compounds; marine natural products


**5. Conclusion**

Value-based contracts offer providers of healthcare services an opportunity for higher rewards than traditional payment models, but with considerable additional risk. Risk comes in many forms, from definitions to execution. This chapter has not touched on performance risk, which is the province of other professionals, mostly clinical. But aside from clinical risk a provider/HCM that accepts value-based risk is open to numerous other forms of risk. The good news is that with appropriate planning and modeling these risks can be managed and mitigated. Doing so will allow the provider or healthcare management organization to capitalize on a growing trend in healthcare finance.

*Value-Based Contracting in Health Care DOI: http://dx.doi.org/10.5772/intechopen.103021*


**5.1 Test system 1**

44 Electrical Generation and Distribution Systems and Power Quality Disturbances

1 *NW*

*NG NW*

1 1

12 *Pd Pd PD* + = (36)

*i i Pd Pd Pg Pw P* = =

Where, *l N* is the number of transmission lines; *k g* is the conductance of branch *k* between buses *i* and *j*; *k t* the tap ration of transformer *k*; *Vi* is the voltage magnitude at bus *i; ij*

Pg2 Pg3 PgN

*Pd1=80% PD*

*Length of Vector Control*

max *Pw*

The inequality constraints to be satisfied are all the security constraints related to the state

Fig. 12 shows the two coordinated vectors control structure related to this stage, the

The proposed algorithm is developed in the Matlab programming language using 6.5 version. The proposed approach has been tested on many practical electrical test systems (small size:

individual of the combined vector control denoted by *X P PQ Q pq w wN STC STCN* = [ 1 1 ,..., , ,..., ] Where [*P P w wN* 1 ,..., ] indicate active power outputs of all units based wind source, [*Q Q STC STCN* 1 ,..., ] represent reactive power magnitude settings of all STATCOM controllers

min *QStatcom*

*i Pd Pw* =

*i*

*i i loss*

= (34)

δthe

+− − = (35)

*Length of Vector Control*

*PD*

*QSTC*

*QSTC QSTC QSTC*

 *1 2 NStc*

*Length of Vector Control*

*NW=NStc*

max *QStatcom*

max *Pg*

2

1 2

voltage angle difference between buses *i* and *j.*

min *Pg*

varaibles and the control variables mentioned in section 2.1.

Fig. 11. Vector control structure: conventional source

*Pw1 Pw2 Pwn*

 *1 2 NW*

*Pd2<=20% PD*

min *Pw*

Fig. 12. Coordinated vector control

exchanged with the network.

**5. Simulation results**

The first test system has 6 generating units; 41 branch system, the system data taken from (). It has a total of 24 control variables as follows: five units active power outputs, six generator-bus voltage magnitudes, four transformer-tap settings, nine bus shunt FACTS controllers (STATCOM). The modified IEEE 30-Bus electrical network is shown in Fig 13.

Fig. 13. Single line diagram for the modified IEEE 30-Bus test system (with FACTS devices)

Optimal Location and Control of

**676**

**0**

Condition: IEEE 30-Bus

**20**

**40**

**60**

**80**

**Power Transit (Pij)**

**100**

**120**

**140**

source and STATCOM

**678**

**680**

**682**

**684**

**Cost (\$/h)**

**686**

**688**

**690**

**692**

**694**

Multi Hybrid Model Based Wind-Shunt FACTS to Enhance Power Quality 47

**0 20 40 60 80 100**

**With Wind-Statcom**

**Without: Wind/Statcom**

**Pij Max**

**Iteration**

**0 5 10 15 20 25 30 35 40 45**

**Branches (i-j)**

Fig. 15. Active power transit (Pij) with and without wind and STATCOM, Case1: Normal

Fig. 14. Convergence characteristic of the 6 generating units with consideration of wind

#### **Case1: Normal Condition**

Table 1. Power Quality Results based Hybrid Model: Wind Source: STATCOM: IEEE-30Bus: Normal Condition

STATCOM **10 12 15 17 20 21 23 24 29**

Q (MVAR) 35.92 -16.27 -9.82 -19.34 -1.96 -19.94 1.25 4.82 -4.25

Pw (MW) 3.9194 3.9202 4.0070 4.0615 4.1781 4.1956 3.9707 3.8700 3.8776

V (p u) 1.02 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Ploss (MW) **7.554**

Cost (\$/h) **676.4485**

**Buses**

**36 (12.7%), PD =283.4 MW**

> **Case1 Normal Condition**

**254.95 (89.96%)**

Table 1. Power Quality Results based Hybrid Model: Wind Source: STATCOM: IEEE-30Bus:

**Case1: Normal Condition**

1 *NW w i P* =

(MW)

Pg1 (MW) 149.92

Pg2 (MW) 46.53

Pg5 (MW) 20.64

Pg8 (MW) 15.81

Pg11(MW) 10.05

Pg13(MW) 12

Qg1 5.39

Qg2 21.67

Qg5 23.04

Qg8 45.77

Qg11 15.43

Qg13 39.09

Normal Condition

1 *NG G i P* =

(MW)

Fig. 14. Convergence characteristic of the 6 generating units with consideration of wind source and STATCOM

Fig. 15. Active power transit (Pij) with and without wind and STATCOM, Case1: Normal Condition: IEEE 30-Bus

Optimal Location and Control of

 = *NW i Pw* 1 (MW)

Pg1 (MW) 64.12

Pg2 (MW) 67.98

Pg5 (MW) 26.86

Pg8 (MW) 34.65

Pg11(MW) 21.00

Pg13(MW) 20.24

Qg1 1.76

Qg2 41.3

Qg5 20.98

Qg8 35.55

Qg11 8.08

Qg13 39.26

 = *NG i PG* 1 (MW)

Multi Hybrid Model Based Wind-Shunt FACTS to Enhance Power Quality 49

STATCOM **10 12 15 17 20 21 23 24 29**

Q (MVAR) 42.76 -15.65 -11.00 -20.10 -2.90 -20.83 0.28 4.09 -4.52

Pw (MW) 5.8791 5.8803 6.0105 6.092 6.2671 6.2934 5.9560 5.8050 5.8164

V (p u) 1.02 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Ploss (MW) **5.449**

Cost (\$/h) **686.1220**

**Buses**

**54 MW (19.05%), PD =283.4 MW**

> **Abnormal Condition Without line 1-2**

**235.610MW (83.14%)**

Table 2. Power Quality Results based Hybrid Model: IEEE-30Bus: Abnormal Condition

Table 1 shows the results based on the flexible integration of the hybrid model, the goal is to have a stable voltage at the candidate buses by exchanging the reactive power with the network, the active power losses reduced to 7.554 MW compared to the base case: 10.05 MW, without integration of the hybrid controllers, the total cost also reduced to 676.4485 \$/h compared to the base case (802.2964 \$/h), Fig. 14 shows the convergence characteristic of fuel cost for the IEEE 30-Bus with consideration of the hybrid models, Fig. 15 shows the distribution of power transit in the different branches at normal condition, Fig. 17 shows the distribution of power transit in the different branches at contingency situation (without line 1-2).

The active power transit reduced clearly compared to the case without integration of wind source which enhance the system security. Fig. 16 shows the improvement of voltage profiles based hybrid model. Results at abnormal conditions (contingency) are also encouragement.

Fig. 16. Voltage profiles with and without hybrid model (wind and STATCOM): IEEE 30-Bus

#### **Case2: Under Contingency Situation**

The effeciency of the integrated hybrid model installed at different critical location is tested under contingency situation caused by fault in power system, so it is important to maintain the voltage magnitudes and power flow in branches within admissible values. In this case a contingency condition is simulated as outage at different candidate lines. Table 2 shows sample results related to the optimal power flow solution under contingency conditions **(***Fault at line 1-2***).**

Table 1 shows the results based on the flexible integration of the hybrid model, the goal is to have a stable voltage at the candidate buses by exchanging the reactive power with the network, the active power losses reduced to 7.554 MW compared to the base case: 10.05 MW, without integration of the hybrid controllers, the total cost also reduced to 676.4485 \$/h compared to the base case (802.2964 \$/h), Fig. 14 shows the convergence characteristic of fuel cost for the IEEE 30-Bus with consideration of the hybrid models, Fig. 15 shows the distribution of power transit in the different branches at normal condition, Fig. 17 shows the distribution of power transit in the different branches at contingency situation (without

The active power transit reduced clearly compared to the case without integration of wind source which enhance the system security. Fig. 16 shows the improvement of voltage profiles based hybrid model. Results at abnormal conditions (contingency) are also

Without/Wind, STATCOM **Max V**

With Wind-STATCOM

**0 5 10 15 20 25 30**

**Bus N°**

The effeciency of the integrated hybrid model installed at different critical location is tested under contingency situation caused by fault in power system, so it is important to maintain the voltage magnitudes and power flow in branches within admissible values. In this case a contingency condition is simulated as outage at different candidate lines. Table 2 shows sample results related to the optimal power flow solution under contingency conditions

Fig. 16. Voltage profiles with and without hybrid model (wind and STATCOM):

**Min V**

line 1-2).

encouragement.

**0.92**

**Case2: Under Contingency Situation**

IEEE 30-Bus

**(***Fault at line 1-2***).**

**0.94**

**0.96**

**0.98**

**1.02**

**Voltage (pu)**

**1.04**

**1.06**

**1.08**

**1.1**

**1**

Table 2. Power Quality Results based Hybrid Model: IEEE-30Bus: Abnormal Condition

Optimal Location and Control of

3571-3577, 2008.

1699, Nov. 2005.

*Simulation*, Elsevier, 2009.

*IEEE IECON Proceeding* , 2010.

7, pp. 1370–1380, July 2010.

Multi Hybrid Model Based Wind-Shunt FACTS to Enhance Power Quality 51

Bansal, R. C., Otimization methods for electric power systems: an overview, *International Journal of Emerging Electric Power Systems*, vol. 2, no. 1, pp. 1-23, 2005. Bent, S., Renewable energy: its physics, use, environmental impacts, economy and planning

C. Chien Kuo, A novel string structure for economic dispatch problems with practical

Chen, A., Blaadjerg, F, Wind farm-A power source in future power systems, *Renewable and*

Chiang C.-L., Improved genetic algorithm for power economic dispatch of units with valve-

Coelho, L. S., R. C. Thom Souza, and V. Cocco Mariani, (2009) Improved differential

Gaing, Z. L., Particle swarm optimization to solving the economic dispatch considering the

Gonzalez, F. D., M. M. Rojas, A. Sumper, O. Gomis-Bellmunt, L. Trilla, Strategies for reactive

Gupta, A., Economic emission load dispatch using interval differential evolution algorithm, *4th International Workshop on reliable Engineering Computing* (REC 2010). Hingorani NG, Gyugyi L (1999) Understanding FACTS: Concepts and Technology of

Hingorani, N.G., FACTS: flexible ac transmission systems, *EPRI Conference on Flexible AC*

Huneault, M., and F. D. Galiana, A survey of the optimal power flow literature, *IEEE Trans.*

Mahdad, B., K. Srairi, T. Bouktir, and and M. EL. Benbouzid, Fuzzy Controlled Parallel PSO

Mahdad, B., T. Bouktir, K. Srairi, and M. EL. Benbouzid, Dynamic Strategy Based Fast

Mahdad, B., T. Bouktir, K. Srairi, OPF with Environmental Constraints with SVC Controller

*Energy Congress -Sweden*, 8-11 May 2011, Linköping, Sweden, Mai 2011. Mahdad, B., T. Bouktir, K. Srairi, Optimal Power Flow for Large-Scale Power System with

Momoh, J. A., and J. Z. Zhu, Improved interior point method for OPF problems, *IEEE Trans.*

*Energy Systems* (IJEPES), vol. 32, no. 4, pp. 507– 517, Juin 2010.

*Power Syst.* , vol. 14, pp. 1114-1120, Aug. 1999.

to Solving Large Practical Economic Dispatch, Accepted and will be Published at

Decomposed GA Coordinated with FACTS devices to enhance the Optimal Power Flow, *Intenational Journal of Energy Conversion and Management*(IJECM), vol. 51, no.

using Decomposed Parallel GA: Application to the Algerian Network*. Journal of Electrical Engineering & Technology*, Korea, Vol. 4, No.1, pp. 55~65, March 2009. Mahdad, B., T. Bouktir, K. Srairi, Optimal Location and Control of Multi Hybrid Model

Based Wind-Shunt FACTS to Enhance Power Quality. Accepted at *World Renewable*

Shunt FACTS using Efficient Parallel GA, *Intenational Journal of Electrical Power &*

Flexible A Transmission Systems. IEEE Computer Society Press.

*Transmission System*, Cincinnati, OH, November 1990.

*Power Systems*, vol. 6, no. 2, pp. 762-770, May 1991.

constraints, *International Journal of Energy Conversion and management*, ,vol. 49, pp.

point effects and multiple fuels, *IEEE Trans. Power Syst.*, vol. 20, no. 4, pp. 1690–

evoluation approach based on clutural algorithm and diversity measure applied to solve economic load dispatch problems, *Journal of Mathemtics and Computers in*

generator constraints, *IEEE Trans. Power Systems*, vol. 18, no. 3, pp. 1187-1195, 2003.

aspects, 3rd ed. UK/USA: Academic Press/Elsevier; 2004.

*Sustainable Energy Reviews*. pp. 1-13, 2008.

power control in wind farms with STATCOM,

Fig. 17. Active power transit (Pij) with hybrid model: Case 2: Abnormal Condition: without line 1-2: IEEE 30-Bus


*2.7. RGB Color Analysis*

The color analysis of microgreens showed that light significantly affected the RB components of amaranth, while no significant differences were observed for the RGB components of turnip greens (Figure 8). In amaranth the white light led to the highest R and B values, while the red light lowered the B value. In the comparison between species, the G component was significantly higher in turnip greens compared to amaranth (Figure 8).

When all the effects were summarized in a PCA score plot, differential reactions of amaranth and turnip greens to different light spectra were observed (Figure 9a,b). The first two PCs were related with eigen values >1 and explained more than 90% of the total variance, with PC1 and PC2 accounting for 56.4% and 43.6% for amaranth, and 54.1% and 45.9% for turnip greens. In amaranth, we identified four groups of positively correlated variables: (1) the group in the upper left quadrant, which included Chl *b*, sugars, and Na; (2) the group in the upper right quadrant, which included carotenoids, total Chl, Chl *a*, Asc, DPPH, TPC, Ca, and Cu; (3) the group clustered in the lower right quadrant, which included nitrates and most mineral elements (Fe, Mg, Zn, Mn, and Ni); (4) the group in the lower left quadrant, which included P, K, FW, and % DW (Figure 9a).

*Plants* **2021**, *10*, 1584

The mean values associated with the two factors and their interaction were evaluated according to Tukey's test. Means significantly different are indicated by different letters; ns not significant, significant at *p* ≤ 0.05 (\*), 0.01 (\*\*), and 0.001 (\*\*\*). Three biological replicates were used for the analysis (*n* = 3).

*2.7. RGB Color Analysis*

8).

**Figure 8.** RGB component analysis of microgreen photos grown under different light conditions: white (W), blue (B), and red (R). Values are means with standard errors (*n* = 4). Four biological replicates were used for the analysis. Data were subjected to two-way ANOVA. Differences among means were determined using Tukey's test. Different letters highlight significant differences at *p* ≤ 0.05; ns not significant, significant at *p* ≤ 0.05 (\*), 0.01 (\*\*), and 0.001 (\*\*\*). **Figure 8.** RGB component analysis of microgreen photos grown under different light conditions: white (W), blue (B), and red (R). Values are means with standard errors (*n* = 4). Four biological replicates were used for the analysis. Data were subjected to two-way ANOVA. Differences among means were determined using Tukey's test. Different letters highlight significant differences at *p* ≤ 0.05; ns not significant, significant at *p* ≤ 0.05 (\*), 0.01 (\*\*), and 0.001 (\*\*\*).

The color analysis of microgreens showed that light significantly affected the RB components of amaranth, while no significant differences were observed for the RGB components of turnip greens (Figure 8). In amaranth the white light led to the highest R and B values, while the red light lowered the B value. In the comparison between species, the G component was significantly higher in turnip greens compared to amaranth (Figure

*Plants* **2021**, *10*, x. https://doi.org/10.3390/xxxxx www.mdpi.com/journal/plants

When all the effects were summarized in a PCA score plot, differential reactions of amaranth and turnip greens to different light spectra were observed (Figure 9a,b). The first two PCs were related with eigen values >1 and explained more than 90% of the total variance, with PC1 and PC2 accounting for 56.4% and 43.6% for amaranth, and 54.1% and 45.9% for turnip greens. In amaranth, we identified four groups of positively correlated variables: (1) the group in the upper left quadrant, which included Chl *b*, sugars, and Na; (2) the group in the upper right quadrant, which included carotenoids, total Chl, Chl *a*, Asc, DPPH, TPC, Ca, and Cu; (3) the group clustered in the lower right quadrant, which included nitrates and most mineral elements (Fe, Mg, Zn, Mn, and Ni); (4) the group in

the lower left quadrant, which included P, K, FW, and % DW (Figure 9a).

**Figure 9.** Principal component loading plot and scores of PCA fresh weight and dry biomass, H/DW, photosynthetic pigments (Chl *a*, Chl *b*, total Chl, and carotenoids), mineral concentrations (nitrate, Na, Mg, P, K, Ca, Mn, Fe, Ni, Cu, and Zn), DPPH, TPC, total sugars, Asc, and total phenolic concentrations for amaranth (**a**) and turnip greens (**b**) as modulated by LED treatments. W = white LED treatment; B = blue LED treatment; R = red LED treatment. **Figure 9.** Principal component loading plot and scores of PCA fresh weight and dry biomass, H/DW, photosynthetic pigments (Chl *a*, Chl *b*, total Chl, and carotenoids), mineral concentrations (nitrate, Na, Mg, P, K, Ca, Mn, Fe, Ni, Cu, and Zn), DPPH, TPC, total sugars, Asc, and total phenolic concentrations for amaranth (**a**) and turnip greens (**b**) as modulated by LED treatments. W = white LED treatment; B = blue LED treatment; R = red LED treatment.

For turnip greens, we identified the following groups: (1) the group in the upper left quadrant, which included K and Na; (2) the group in the upper right quadrant, which included sugars, nitrates, % DW, and some mineral elements (Ca, Zn, and Mn); (3) the group clustered in the lower right quadrant, which included carotenoids, Chl *a*, H/DW, FW, and most mineral elements (Cu, Ni, Fe, Mg, and Na); (4) the group in the lower left quadrant, which included antioxidant activity (DPPH and TPC), Chl *b*, total Chl, and Asc (Figure 9b).

Plants of amaranth grown under red LED, positioned in the lower left quadrant of the PCA score plot, exhibited a higher concentration of P and K, whilst those grown under blue LED, positioned in the upper right quadrant, were characterized by higher total Chl, Chl *a*, and antioxidant activity. Plants of turnip greens, grown under blue LED, positioned in lower left quadrant, were characterized by higher antioxidant level (TPC and Asc), antioxidant activity (DPPH), and total Chl, whilst those grown under red LED, positioned in the lower right quadrant of the PCA score plot, showed a higher content of mineral elements (Cu, Ni, Fe, Mg, and Na).

The PCA analysis reported in the present study could, therefore, help to better understand the influence of LED treatments on morphological and nutraceutical characteristics of the two studied species.


**6. Conclusions**

The presented work highlights the capabilities of UAS technology to match the needs of land professionals in Rwanda. Results of a sound needs assessment across different stakeholder groups demonstrate prioritised demands of respective respondents. Although ranked differently, the need for high-resolution, up-to-date land information is consistently identified in the final lists of all group discussions. Across the globe, UAS have become an attractive technology and only the upcoming years will show whether multiple governmental and non-governmental stakeholders can capitalise on the numerous benefits of this data acquisition method. The flight missions in Rwanda showed that UAS as a platform to remotely capture images have clear advantages in terms of fit-for-purpose data provision by facilitating accurate, up-to-date data with a potentially high spatial as well as temporal resolution. However, the integration of the needs assessment and the UAS test flights indicates that structural and capacity conditions currently undermine the vast potential of the UAS data acquisition method. Therefore, a key policy priority should be to implement country-specific capacity development and governance strategies; otherwise, scaled implementation and increasing technology uptake might remain wishful thinking. Notwithstanding the outlined challenges, the results of this study show that UAS technology has the potential to be an appropriate land tool with a significant contribution in catering the base data for most of the prioritized land information needs in Rwanda.

**Author Contributions:** Conceptualization, C.S. (Claudia Stöcker), M.K., R.B. and J.Z.; Data curation, C.S. (Claudia Stöcker); Formal analysis, C.S. (Claudia Stöcker) and S.H.; Funding acquisition, R.B.; Investigation, C.S. (Claudia Stöcker), S.H., P.N. and C.S. (Cornelia Schmidt); Project administration, M.K.; Supervision, M.K. and J.Z.; Visualization, C.S. (Claudia Stöcker); Writing–original draft, C.S. (Claudia Stöcker) and S.H.; Writing–review & editing, C.S. (Claudia Stöcker), S.H., P.N., C.S. (Cornelia Schmidt), M.K., R.B. and J.Z.

**Funding:** The research described in this paper was funded by the research project "its4land," which is part of the Horizon 2020 program of the European Union, project number 687828.

**Acknowledgments:** The authors thank Charis UAS Ltd. in Rwanda for the technical realization and support of all UAS test flights for this research investigation.

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

## **References**

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