engine performance fueled with jojoba biodiesel and
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lable at ScienceDirect
Energy 239 (2022) 122390
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Energy
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Engine performance fueled with jojoba biodiesel and enzymaticsaccharification on the yield of glucose of microbial lipids biodiesel
Milos Milovancevic a, Yousef Zandi b, j, *, Abouzar Rahimi b, j, Neboj�sa Deni�c c,Vuk Vujovi�c d, Dragan Zlatkovi�c d, Ivana D. Ilic e, Jelena Stojanovi�c d, Sne�zana Gavrilovi�c f,Mohamed Amine Khadimallah g, h, Vladan Ivanovi�c i
a University of Ni�s, Faculty of Mechanical Engineering, Serbiab Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iranc University of Pri�stina in Kosovska Mitrovica, Faculty of Sciences and Mathematics, Serbiad Alfa BK University, Belgrade, Serbiae Department for Mathematics and Informatics, Faculty of Medical Sciences, University of Nis, Serbiaf Academy Sumadija, Serbiag Prince Sattam Bin Abdulaziz University, College of Engineering, Civil Engineering Department, Al-Kharj, 16273, Saudi Arabiah Laboratory of Systems and Applied Mechanics, Polytechnic School of Tunisia, University of Carthage, Tunis, Tunisiai Ministry of the Interior of the RS, Emergency Situations Office Ni�s, Serbiaj Robotics & Soft Technologies Research Center, Tabriz Branch, Islamic Azad University, Tabriz, Iran
a r t i c l e i n f o
Article history:Received 30 March 2021Received in revised form31 July 2021Accepted 19 October 2021Available online 20 October 2021
Keywords:BiodieselJojoba oilEngine emissionEnzymatic saccharificationGlucose
* Corresponding author. Department of Civil EnginAzad University, Tabriz, Iran
E-mail addresses: [email protected] (M. Milovancevi
https://doi.org/10.1016/j.energy.2021.1223900360-5442/© 2021 Elsevier Ltd. All rights reserved.
a b s t r a c t
The study's major purpose was to find the best predictors for biodiesel efficiency based on emissionvariables and using jojoba oil as a fuel. Given the importance of biodiesel in reducing carbon dioxideemissions, a more thorough examination of such engines is required. As a result, the study's major goalwas to use a selection technique to determine the best predictors for brake thermal efficiency (%), un-burnt hydrocarbons (ppm vol.) and oxides of nitrogen (ppm vol.) of the biodiesel engine. For such apurpose several factors are selected and analyzed. The input variables are blending (%), fuel injectiontiming (obTDC), fuel injection pressure (bar) and engine load (%). The analyzing procedure was performedby adaptive neuro fuzzy inference system (ANFIS) and all available parameters are included. The ANFISmodel could be used as simplification of the analysis since there is no need for knowledge of internalphysical and chemical characteristics of the biodiesel engine. The results from the function clearlyindicate that the input attribute “Engine load” (RMSE ¼ 1.8002) is the most influential for the brakethermal efficiency. Furthermore, the input attribute “Fuel injection pressure” (RMSE ¼ 4.2620) is themost influential for the unburnt hydrocarbons. “Engine load” (RMSE ¼ 4.7484) is the most influential forthe oxides of nitrogen. In this paper, an adaptive neuro fuzzy inference system (ANFIS) was used todevelop a prediction approach for determining the influence of hydrolysis time, cellulase loading, b-Glucosidase loading, substrate loading and working volume on the enzymatic saccharification on theyield of glucose. The ideal combination of two input attributes or two predictors for enzymaticsaccharification on glucose yield was discovered to be “substrate loading” and “working volume”(RMSE ¼ 4.1625). The findings could be useful in reducing the cost of the procedure by optimizingenzymatic saccharification on glucose response yield.
© 2021 Elsevier Ltd. All rights reserved.
eering, Tabriz Branch, Islamic
c), [email protected] (Y. Zandi).
1. Introduction
Since the consumption of fossil fuels is increasing there is needfor alternative fuels in order to fulfill the requirements [1e4]. Thealternative fuels should be cost effective and energy sustainable.Biodiesel is one of the most known alternative fuels. However, theproduction of biodiesel depends on the accessibility of feedstuffs
M. Milovancevic, Y. Zandi, A. Rahimi et al. Energy 239 (2022) 122390
where oils represent the important role. To improve the efficiencyof the biodiesel engines there is need for the more comprehensiveanalysis [5e8].
Biodiesel is one of the most known alternative fuel which iscreated from biomass wastes by trans-esterification of triglyceridesinto fatty acid methyl esters or FAME. The biodiesel could representthe solution to the fossil fuel crisis and to decrease global warming[9e11]. Production of the biodiesel is based mostly from wastecooking oils or lipids from microorganisms. However, productioncost of biodiesel up to 70% because of the cost of edible oilsincreased. Hence production of biodiesel from microbial lipidsrepresents the alternative for the sustainable biodiesel production.
The high demand for the use of petroleum products has pushedcurrent research into environmentally friendly fuels suited fordiesel engine performance [12e15]. In investigation [16], jojobabiodiesel-diesel bends were fueled to a four-stroke single cylindercompression ignition engine at compression ratio of 17.5 to un-derstand its emission and performance phenomenon and the per-formance parameters includes brake thermal efficiency, brakespecific fuel consumption and emission parameters includes un-burned hydrocarbon, carbon monoxide, oxides of nitrogen andsmoke were assessed. Green Fe3O4 NPs were generated from dis-carded Ziziphus mauritiana seeds in study [17], and these NPs wereused to improve the jojoba methyl ester blend characteristics. Thehigh viscosity, poor volatility, and significant NOx production ofJojoba oil in CI engines are the primary problems [18]. A study [19]looked at the performance and emissions of a direct injection dieselengine running on diesel-jojoba-butanol mixes. In the recent de-cades, Jojoba biodiesel is considered as one of the most potentialrenewable energy sources of many countries hence there wasinvestigation conducted for the enhancement of jojoba biodiesel-diesel blends with the addition of 10% n-butanol by volumewhich is generally acknowledged as an oxygenated additive [20].The results of B20 blend shows that there was decrease in cylindertemperature by 8.2% for Jatropha curcas, engine knocking tendencyby 13.47% for rapeseed, smoke by 63.85% for jojoba oil and NOXemission by 42.2% for Jatropha curcas compared to those fromdiesel fuel at CR17.5 with full engine load [21]. Jojoba oil is obtainedby cultivation of the jojoba plant, harvesting of seeds followed byextraction of oil, which is then converted to biodiesel by the processof transesterification [22]. The significant reduction of smokeemission by 55.3% and PM emission by 51.8% were seen for jojoba,NOX was reduced by 39.6% for Jatropha curcas and SO2 by 46.54%for fish oil at full load conditions with the compression ratio of 17.5powered with B20 blend of biodiesel [23]. Non-edible Jojoba bio-diesel blended with diesel was tested for use as a substitute fuel fordiesel engines [24].
Biodiesel production using microbial oil derived from food
Table 1Chemical properties of the test fuels.
Fuel Density at25 �C (kg/m3)
Calorificvalue (kJ/kg)
Kinematic Viscosity at40 �C (mm2/s)
Sapnu
Diesel 846 43 2.9 18010% blending (Jojoba
biodiesel) with diesel852 42 3.01 187
20% blending (Jojobabiodiesel) with diesel
858 40 3.03 196
30% blending (Jojobabiodiesel) with diesel
867 38 3.1 200
40% blending (Jojobabiodiesel) with diesel
872 37 3.26 214
50% blending (Jojobabiodiesel) with diesel
881 35 3.41 180
2
waste discarded by the hospitality sector could provide a sustain-able replacement for diesel fuel [25e28]. In article [29] has beenidentified a novel oily yeast strain AM2352 of Rhodotorula taiwa-nensis, which could effectively convert corncob hydrolysate intomicrobial lipid where it was proved that the over 81.5% of theextracted oil could be converted into biodiesel. Volatile fatty acids(VFAs) derived from organic wastes are being considered as low-cost feedstock for microbial lipid production as a valuable alter-native to plant derived oils/biodiesel [30]. Based on the review [31]on microbial biodiesel technologies, an integrated biodiesel pro-duction line incorporating all the critical technical steps is proposedfor unified management and continuous optimization for highlyefficient biodiesel production. The results in article [32] suggestthat termed orange peel extract (OPE)constitutes an excellent basisfor the fed-batch production of R. toruloides lipids, and this processmight afford a further option in OPW-based biorefinery. To realizelignocellulosic biorefinery is of global interest, with enzymaticsaccharification presenting an essential stage to convert polymericsugars to mono-sugars for fermentation use [33]. Chemo-enzymatic saccharification and bioethanol fermentation of the re-sidual biomass of Dunaliella tertiolecta after lipid extraction forbiodiesel production were investigated [34]. In study [35], glucoseacid catalyst was synthesized via modified sulfonation approachand was successfully utilized in a continuous oscillatory flowreactor to produce an environmentally friendly biodiesel underatmospheric conditions. In recent decades, Artificial intelligence(AI) has been widely developed in various fields such as engineer-ing, health care, decision making, signal processing, etc [36e41]. AImodels compared to experimental techniques and numerical ap-proaches like finite element method (FEM) are able to producemore accurate results [42e47]. Many investigations were deployeddifferent types of AI models and reliable results were obtained,which showed their capability in data prediction [48e53]. In fact, AIalgorithms simulate human intelligence using computer tools[54e56].
A selection approach for best predictors for biodiesel efficiencybased on emission factors and with jojoba oil fuel is used in thisstudy. The data set for the selection procedure was gathered andorganized from published literature. The data samples are selectedusing the adaptive neural fuzzy inference system (ANFIS) [57e60].
Enzyme loading, hydrolysis time, biomass loading, workingvolume, and agitation were employed as independent variables inthe study. The result is enzymatic saccharification of microbiallipids' glucose production biodiesel.
onificationmber
Iodinevalue
Flashpoint (oC)
CetaneIndex
Cloudpoint (oC)
Pourpoint(oC)
Auto-ignitiontemperature (oC)
122 77 53.6 6.3 �5.4 262133 81 49.2 7.1 �4.3 278
137 85 45.5 7.7 �3.3 284
138 89 43.1 7.9 �2.4 291
139 94 42.5 8.3 �1.4 294
122 103 40.3 9.3 1.4 316
Fig. 1. Flowchart of the used methodology.
Fig. 2. Experimental setup of the engine.
Table 2Diesel engine configuration [61].
Type CylinderBore
StrokeLength
Compressionratio
Swept Volume Rated power Crank anglesensor
Fuel injectiontiming
Fuel injectionpressure
Method ofcranking
Method ofLubrication
4S, DI dieselengine
87.6 mm 110 mm 17.5:1 661 Cubiccentimeter
5.2 kW at1500 rpm
Resolution 1� ,TDC pulse
23 obTDC 18 Mpa Manual Forced type
M. Milovancevic, Y. Zandi, A. Rahimi et al. Energy 239 (2022) 122390
2. Methodology and materials
2.1. Experimental procedure for jojoba biodiesel
Dried jojoba oil was used as the biodiesel in the study from NewDelhi, India. The free fatty acid of the oil was 4.62%. The jojobabiodiesel is obtained under conditions presented in study [61]. Theextra stirring has been undertaken for 20min to get the desired freefatty acid value. The biodiesel from the jojoba oil is acquired andsegregated by gravity from glycerol for 12 h in order to disjoint theglycerol for different applications. Hot distilled water is used toremove methanol after warming to 120 �C for 25min. Physico-chemical properties of the jojoba biodiesel are estimated based onthe ASTM standards (ASTM D941, ASTM 240, ASTM D445). Table 1shows chemical characteristics of the sample fuel.
Fig. 1 shows the flowchart of the methodology used in the study.The most important task is to acquire the relevant data with allpossible combinations of the inputs. Fig. 2 shows the experimentalsetup of the engine. Table 2 shows diesel engine configuration.
Literature [61] is used to collect and organize data samples forANFIS analysis. Table 3 illustrates examples of input and outputdata. Blending (percent), fuel injection timing (obTDC), fuel injec-tion pressure (bar), and engine load are the inputs used (percent).The outputs are the brake thermal efficiency (%), unburned hy-drocarbons (ppm vol.), and nitrogen oxides (ppm vol). (ppm vol.).
3
2.2. Data samples of enzymatic saccharification
The adaptive neuro fuzzy inference system (ANFIS) was utilizedto estimate the influence of independent factors on the responseand to comprehend the interactions between them. Enzymeloading, hydrolysis time, biomass loading, working volume, andagitation were employed as independent variables in the study. Byconducting 52 tests at five distinct levels, these parameters wereevaluated on five separate levels. Table 4 shows the experimentalmatrix of the variables along with the experimental findings. Theoutput shows enzymatic saccharification of microbial lipids bio-diesel glucose production.
2.3. ANFIS methodology
As shown in Fig. 3, the ANFIS network contains five levels. Thefuzzy inference system lies at the heart of the ANFIS network. Layer1 accepts the inputs and uses membership functions to convertthem to fuzzy values. The bell-shaped membership function isemployed in this study because it has the best capability fornonlinear data regression.
Bell-shaped membership functions is defined as follows:
Table 3Experimental data samples [61].
Input 1 Input 2 Input 3 Input 4 Output 1 Output 2 Output 3
Blending(%)
Fuel injection timing(obTDC)
Fuel injection pressure(bar)
Engine load(%)
Brake thermal efficiency(%)
Unburnt hydrocarbons (ppmvol.)
Oxides of nitrogen (ppmvol.)
30 27 220 80 32.5 46 67520 23 200 60 27.1 42 64820 23 200 60 26.9 42 64420 31 200 60 28.2 40 65330 27 180 40 22.7 53 51310 27 180 40 23.7 55 49930 27 180 80 31.4 53 67910 19 220 40 16.5 53 49910 27 180 40 21 57 51120 23 160 60 25.3 52 63720 23 200 60 27.1 41 64520 15 200 60 25.3 51 63210 19 220 80 25.6 47 67820 23 200 60 27 42 65120 23 240 60 27.3 42 66030 19 180 80 29.3 46 67710 27 220 60 31.6 48 65640 23 200 60 23.5 39 65310 27 180 80 31.1 55 67620 23 200 20 14 50 39020 23 200 100 31.5 49 78110 27 220 40 19.2 44 50820 23 200 60 27.2 41 64720 23 200 60 27 43 64530 19 220 40 23 40 50830 19 220 80 32.5 38 66630 27 220 40 21 35 51710 19 180 80 26.2 55 66620 23 200 60 27.1 47 64330 19 180 40 21.8 43 50020 23 200 60 29.7 55 641
M. Milovancevic, Y. Zandi, A. Rahimi et al. Energy 239 (2022) 122390
mðxÞ¼ bellðx; ai; bi; ciÞ¼1
1þ"�
x�ciai
�2#bi
(1)
where fai; bi; cig is the parameters set and x is input.The second layer multiplies the first layer's fuzzy signals and
produces the rule's firing strength. The rule layers are the thirdlayer, and they normalize all of the signals from the second layer.The fourht layer does rule inference and converts all signals to crispvalues. The last layers summed all of the signals and provided aclean output value.
3. Results
The best predictors for brake thermal efficiency, unburned hy-drocarbons, and nitrogen oxides were chosen using the ANFISapproach. The selection is crucial, as is the preprocessing of theinput parameters to eliminate irrelevant inputs. The data set istaken from Table 1's data file. Following the commands in MATLABSoftware, the dataset is partitioned into a training set (odd-indexedsamples) and a checking set (even-indexed samples):
[trn_data ¼ data(1:2:end,:);[chk_data ¼ data(2:2:end,:);The function “exhsrch” conducts an exhaustive search within
the available inputs to identify the set of inputs that have thegreatest impact on brake thermal efficiency, unburned hydrocar-bons, and nitrogen oxides. The function's first parameter definesthe number of input combinations that will be tested during theselection process. In essence, “exhsrch” creates an ANFIS model for
4
each combination, trains it for one epoch, and then publishes theresults. The command lines beloware used to find the one, two, andthree most important attributes in forecasting the outcome:
[ exhsrch(1,trn_data, chk_data);[ exhsrch(2,trn_data, chk_data);[ exhsrch(3,trn_data, chk_data);Tables 5e7 shoed results for each output based on the one and
two inputs. With respect to the output, the input variable with thesmallest training error is the most relevant. The function's findingsclearly show that the input attribute “Engine load” (RMSE¼ 1.8002)has the greatest influence on brake thermal efficiency. Further-more, for unburned hydrocarbons, the input attribute “Fuel injec-tion pressure” (RMSE ¼ 4.2620) has the greatest influence. Finally,the input attribute “Engine load” (RMSE ¼ 4.7484) has the greatestinfluence on nitrogen oxides.
Figs. 4e6 depict the ANFIS model's input/output surface at theminimal checking error during the training phase for three outputsbased on the input combinations chosen.
Table 8 shows the effects of enzymatic saccharification on theglucose output of microbial lipids biodiesel. The results show thatthe input characteristic “Substrate loading” has the greatest impacton the enzymatic saccharification of microbial lipids biodieselglucose production. There is no overfitting because the training andchecking errors are comparable. The findings of “exhsrch” showthat “Substrate loading” and “Working volume” are the best twoinput features or parameters to combine. For the purposes ofcomparison, linear regression can be used to corroborate the ANFISregression results. The RMSE of ANFIS against checking data is6.141, while the RMSE of linear regression is 76.595.
Fig. 7 depicts the ANFISmodel's input/output surface at the leastchecking error during the training phase. Fig. 7 shows a nonlinear
Table 4Input and output data samples [62].
Input 1 Input 2 Input 3 Input 4 Input 5 Input 6 output
Hydrolysis time (h) Cellulase loading (U/g) b-Glucosidase loading (U/g) Substrate loading (g/L) Working volume (mL) Agitation (rpm) Glucose (g/L)24 15 75 50 50 150 16.323.66 32.5 47.5 35 125 175 5.1660 32.5 47.5 35 7.62 175 33.2260 32.5 47.5 58.48 125 175 17.1196 15 75 20 50 150 7.8260 32.5 4.46 35 125 175 7.4024 15 20 20 200 200 3.9396 50 75 20 200 150 7.8624 50 20 50 50 150 27.9760 5.11 47.5 35 125 175 1.7260 59.89 47.5 35 125 175 16.0424 15 20 50 50 200 13.8560 32.5 47.5 35 125 175 8.7896 15 20 50 200 200 5.6496 15 75 20 200 200 2.2096 50 20 50 50 200 37.0596 50 75 50 50 150 38.7424 50 20 20 50 200 9.7524 50 75 20 50 150 12.4896 15 20 20 200 150 2.5196 15 20 20 50 200 4.9596 15 75 50 200 150 7.8460 32.5 47.5 11.52 125 175 0.6424 15 20 50 200 150 6.7660 32.5 90.54 35 125 175 8.6996 50 75 50 200 200 13.9396 15 75 50 50 200 18.4524 15 75 20 50 200 4.3260 32.5 47.5 35 125 175 9.3124 50 20 50 200 200 11.5024 50 75 50 200 150 13.8660 32.5 47.5 35 125 175 9.0060 32.5 47.5 35 125 135.87 10.9360 32.5 47.5 35 125 214.13 7.9796 50 20 20 200 200 5.1396 50 20 20 50 150 17.5060 32.5 47.5 35 125 175 9.9296 50 20 50 200 150 16.41116.34 32.5 47.5 35 125 175 9.6224 15 75 50 200 200 6.3696 15 20 50 50 150 20.4760 32.5 47.5 35 125 175 9.9860 32.5 47.5 35 242.38 175 18.8224 50 75 50 50 200 28.2460 32.5 47.5 35 125 175 8.0724 15 75 20 200 150 5.8860 32.5 47.5 35 125 175 9.3424 15 20 20 50 150 4.5996 50 75 20 50 200 15.4624 50 75 20 200 200 5.1660 32.5 47.5 35 125 175 9.5424 50 20 20 200 150 5.08
Fig. 3. ANFIS layers.
M. Milovancevic, Y. Zandi, A. Rahimi et al. Energy 239 (2022) 122390
and monotonic surface that illustrates how the ANFIS model re-sponds to different substrate loading and working volume values.
5
4. Conclusion
Biodiesel production is reliant on the availability of feedstocks,with oils playing a key role. The study's major purpose was to findthe best predictors for biodiesel efficiency based on emission
Table 5ANFIS correlations for brake thermal efficiency.
Blending (%) Fuel injection timing (obTDC) Fuel injection pressure (bar) Engine load (%)
Blending (%) trn ¼ 3.6293, chk ¼ 6.4027Fuel injection timing (obTDC) trn ¼ 3.4876, chk ¼ 6.0817 trn ¼ 3.5329, chk ¼ 6.0572Fuel injection pressure (bar) trn ¼ 3.5407, chk ¼ 6.1516 trn ¼ 0.7352, chk ¼ 4.8788 trn ¼ 3.6496, chk ¼ 5.6209Engine load (%) trn ¼ 1.2169, chk ¼ 3.5731 trn ¼ 0.7352, chk ¼ 4.8788 trn ¼ 1.4330, chk ¼ 7.5378 trn ¼ 1.8002, chk ¼ 2.6350
Table 6ANFIS correlations for unburnt hydrocarbons.
Blending (%) Fuel injection timing (obTDC) Fuel injection pressure (bar) Engine load (%)
Blending (%) trn ¼ 5.5755, chk ¼ 7.5723Fuel injection timing (obTDC) trn ¼ 5.1892, chk ¼ 7.4270 trn ¼ 5.7535, chk ¼ 7.9785Fuel injection pressure (bar) trn ¼ 3.7081, chk ¼ 8.1033 trn ¼ 4.2598, chk ¼ 10.2730 trn ¼ 4.2620, chk ¼ 6.5009Engine load (%) trn ¼ 4.8894, chk ¼ 11.6140 trn ¼ 5.4352, chk ¼ 7.8412 trn ¼ 3.2384, chk ¼ 20.9862 trn ¼ 5.8543, chk ¼ 5.7647
Table 7ANFIS correlations for oxides of nitrogen.
Blending (%) Fuel injection timing (obTDC) Fuel injection pressure (bar) Engine load (%)
Blending (%) trn ¼ 64.9099, chk ¼ 104.9993Fuel injection timing (obTDC) trn ¼ 60.3000, chk ¼ 113.8803 trn ¼ 67.6405, chk ¼ 111.9352Fuel injection pressure (bar) trn ¼ 61.7141, chk ¼ 109.8950 trn ¼ 67.2633, chk ¼ 97.2380 trn ¼ 67.5576, chk ¼ 102.9370Engine load (%) trn ¼ 4.2131, chk ¼ 10.8300 trn ¼ 4.0363, chk ¼ 16.5251 trn ¼ 2.1139, chk ¼ 18.4076 trn ¼ 4.7484, chk ¼ 14.7441
Fig. 4. ANFIS decision surface for brake thermal efficiency.
Fig. 5. ANFIS decision surface for unburnt hydrocarbons.
Fig. 6. ANFIS decision surface for oxides of nitrogen.
M. Milovancevic, Y. Zandi, A. Rahimi et al. Energy 239 (2022) 122390
6
variables and using jojoba oil as a fuel.The best predictors for brake thermal efficiency, unburned hy-
drocarbons, and nitrogen oxides were chosen using the ANFISapproach. According to the findings, the input attribute:
� “Engine load” (RMSE ¼ 1.8002) is the most influential for thebrake thermal efficiency.
� “Fuel injection pressure” (RMSE¼ 4.2620) is themost influentialfor the unburnt hydrocarbons. “
� Engine load” (RMSE ¼ 4.7484) is the most influential for theoxides of nitrogen.
� The best combination of two input qualities or predictors forbrake thermal efficiency is “fuel injection timing” and “engineload".
� The ideal combination of two input qualities or two predictorsfor unburned hydrocarbons is “fuel injection pressure” and“engine load".
Table 8ANFIS correlations for enzymatic saccharification on the yield of glucose of microbial lipids biodiesel.
Hydrolysis time (h) Cellulase loading (U/g) b-Glucosidase loading(U/g)
Substrate loading (g/L) Working volume (mL) Agitation (rpm)
Hydrolysis time (h) trn ¼ 8.8111,chk ¼ 8.8703
Cellulase loading (U/g) trn ¼ 8.2555,chk ¼ 7.8599
trn ¼ 8.2748,chk ¼ 7.7601
b-Glucosidase loading(U/g)
trn ¼ 8.6108,chk ¼ 31.7374
trn ¼ 8.2081,chk ¼ 7.7686
trn ¼ 8.7355,chk ¼ 8.6329
Substrate loading (g/L) trn ¼ 6.4102,chk ¼ 9.1701
trn ¼ 6.0656,chk ¼ 7.9111
trn ¼ 6.4419,chk ¼ 9.6275
trn ¼ 6.6326,chk ¼ 9.1007
Working volume (mL) trn ¼ 6.2794,chk ¼ 7.6086
trn ¼ 5.3272,chk ¼ 6.9335
trn ¼ 6.3342,chk ¼ 7.1772
trn ¼ 4.1625,chk ¼ 6.1693
trn ¼ 6.8289,chk ¼ 7.0789
Agitation (rpm) trn ¼ 7.7077,chk ¼ 11.1643
trn ¼ 7.4929,chk ¼ 9.6172
trn ¼ 7.4958,chk ¼ 12.1265
trn ¼ 6.5002,chk ¼ 8.6910
trn ¼ 5.7447,chk ¼ 9.2929
trn ¼ 7.8718,chk ¼ 10.6152
Fig. 7. ANFIS decision surface for enzymatic saccharification on the yield of glucose ofmicrobial lipids biodiesel.
M. Milovancevic, Y. Zandi, A. Rahimi et al. Energy 239 (2022) 122390
� The ideal combination of two input qualities or two predictorsfor nitrogen oxides is “fuel injection pressure” and “engineload".
� The ideal combination of two input attributes or two predictorsfor brake thermal efficiency is “fuel injection timing,” “fuel in-jection pressure,” and “engine load".
� The ideal combination of two input qualities or two predictorsfor unburned hydrocarbons is “fuel injection time,” “fuel injec-tion pressure,” and “engine load".
� The ideal combination of two input qualities or two predictorsfor nitrogen oxides is “fuel injection timing,” “fuel injectionpressure,” and “engine load".
� The ideal combination of two input qualities or two predictorsfor enzymatic saccharification on glucose yield is “substrateloading” and “working volume.".
The findings could aid in the optimization of enzymaticsaccharification on the yield of glucose in microbial lipids biodiesel,lowering the process's cost. Microbial lipid production has a lot ofpotential for biodiesel production, and it could also help the envi-ronment by reducing waste disposal.
Declaration of competing interest
The authors declare that they have no known competingfinancial interests or personal relationships that could haveappeared to influence the work reported in this paper.
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