Ammonia Based Pretreatment Optimization of Cornstover Biomass Using Response Surface Methodology and Artificial Neural Network

Document Type : Research Paper


School of Chemical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia


Effective pretreatment of lignocellulosic biomass could be used to produce fermentable sugar for renewable energy production, which reduces problems related to nonrenewable fuel. Therefore, the purpose of this study was to produce monosaccharide sugar for renewable energy from agricultural waste via ammonia pretreatment optimization using response surface methodology (RSM) and artificial neural network (ANN). Cornstover was collected and mechanically pre-treated. RSM and ANNwere applied for experimental design and optimum parameters estimation. Cornstover was converted into simple sugars with a combination of ammonia treatment subsequently enzymatic hydrolysis.
The maximum yield of glucose (87.46%), xylose (77.5%), and total sugar (442.0g/Kg) were all accomplished at 20 min of residence time, 4.0 g/g of ammonia loading, 132.5 0C of temperature, and 0.5 g/g of water loading experimentally. While 86.998% of glucose, 76.789% of xylose, and 439.323(g/Kg) of total sugar were achieved by prediction of the ANN model. It was shown that cornstover has a massive potential sugar for the production of renewable fuel.  Ammonia loading had a highly significant effect on the yield of all sugars compared to other parameters.  Interactively, ammonia loading and residence time had a significant impact on the yield of glucose, while water loading and residence time, had a significant effect on the yield of xylose. The accuracy and prediction of an artificial neural network are better than that of the response surface methodology.


[1] Hamelinck CN, Van Hooijdonk G, Faaij AP. Ethanol from lignocellulosic biomass: techno-economic performance in short-, middle-and long-term. Biomass and bioenergy. 2005 Apr 1;28(4):384-410.
[2] McKendry P. Energy production from biomass (part 1): overview of biomass. Bioresource technology. 2002 May 1;83(1):37-46.
[3] Dahunsi SO, Oranusi S, Efeovbokhan VE. Optimization of pretreatment, process performance, mass and energy balance in the anaerobic digestion of Arachis hypogaea (Peanut) hull. Energy Conversion and Management. 2017 May 1;139:260-75.
[4] Avci A, Saha BC, Dien BS, Kennedy GJ, Cotta MA. Response surface optimization of corn stover pretreatment using dilute phosphoric acid for enzymatic hydrolysis and ethanol production. Bioresource technology. 2013 Feb 1;130:603-12.
[5] Saritha M, Arora A. Biological pretreatment of lignocellulosic substrates for enhanced delignification and enzymatic digestibility. Indian journal of microbiology. 2012 Jun;52(2):122-30.
[6] Sindhu R, Binod P, Pandey A. Biological pretreatment of lignocellulosic biomass–An overview. Bioresource technology. 2016 Jan 1;199:76-82.
[7] An S, Li W, Liu Q, Xia Y, Zhang T, Huang F, Lin Q, Chen L. Combined dilute hydrochloric acid and alkaline wet oxidation pretreatment to improve sugar recovery of corn stover. Bioresource technology. 2019 Jan 1;271:283-8.
[8] Wang Z, He X, Yan L, Wang J, Hu X, Sun Q, Zhang H. Enhancing enzymatic hydrolysis of corn stover by twin-screw extrusion pretreatment. Industrial Crops and Products. 2020 Jan 1;143:111960.
[9] Xu H, Li Y, Hua D, Zhao Y, Mu H, Chen H, Chen G. Enhancing the anaerobic digestion of corn stover by chemical pretreatment with the black liquor from the paper industry. Bioresource technology. 2020 Jun 1;306:123090.
[10] Oladi S, Aita GM. Optimization of liquid ammonia pretreatment variables for maximum enzymatic hydrolysis yield of energy cane bagasse. Industrial crops and products. 2017 Sep 1;103:122-32.
[11] Imandi SB, Bandaru VR, Somalanka SR, Garapati HR. Optimization of medium constituents for the production of citric acid from byproduct glycerol using Doehlert experimental design. Enzyme and Microbial Technology. 2007 Apr 3;40(5):1367-72.
[12] Muthusamy S, Manickam LP, Murugesan V, Muthukumaran C, Pugazhendhi A. Pectin extraction from Helianthus annuus (sunflower) heads using RSM and ANN modelling by a genetic algorithm approach. International journal of biological macromolecules. 2019 Mar 1;124:750-8.
[13] Selvaraj R, Moorthy IG, Kumar RV, Sivasubramanian V. Microwave mediated production of FAME from waste cooking oil: modelling and optimization of process parameters by RSM and ANN approach. Fuel. 2019 Feb 1;237:40-9.
[14] Gordon DR, Tancig KJ, Onderdonk DA, Gantz CA. Assessing the invasive potential of biofuel
Journal of Chemical and Petroleum Engineering 2021, 55(1): 83-97 97
species proposed for Florida and the United States using the Australian Weed Risk Assessment. Biomass and Bioenergy. 2011 Jan 1;35(1):74-9.
[15] Nazerian M, Kamyabb M, Shamsianb M, Dahmardehb M, Kooshaa M. Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient optimization of flexural properties of gypsum-bonded fiberboards. Cerne. 2018 Mar;24(1):35-47.
[16] Cheok CY, Chin NL, Yusof YA, Talib RA, Law CL. Optimization of total phenolic content extracted from Garcinia mangostana Linn. hull using response surface methodology versus artificial neural network. Industrial Crops and Products. 2012 Nov 1;40:247-53.
[17] Biomass L. Laboratory Protocols in Fungal Biology. Lab Protoc Fungal Biol. 2013.
[18] Zhao C, Shao Q, Ma Z, Li B, Zhao X. Physical and chemical characterizations of corn stalk resulting from hydrogen peroxide presoaking prior to ammonia fiber expansion pretreatment. Industrial Crops and Products. 2016 May 1;83:86-93.
[19] Weiss ND, Farmer JD, Schell DJ. Impact of corn stover composition on hemicellulose conversion during dilute acid pretreatment and enzymatic cellulose digestibility of the pretreated solids. Bioresource technology. 2010 Jan 1;101(2):674-8.
[20] Garlock RJ, Balan V, Dale BE. Optimization of AFEX™ pretreatment conditions and enzyme mixtures to maximize sugar release from upland and lowland switchgrass. Bioresource Technology. 2012 Jan 1;104:757-68.
[21] Zhang X, Chen J, Mao M, Guo H, Dai Y. Extraction optimization of the polysaccharide from Adenophorae Radix by central composite design. International journal of biological macromolecules. 2014 Jun 1;67:318-22.
[22] Maran JP, Sivakumar V, Thirugnanasambandham K, Sridhar R. Microwave assisted extraction of pectin from waste Citrullus lanatus fruit rinds. Carbohydrate polymers. 2014 Jan 30;101:786-91.
[23] Zhao-Hui X, Xin Z, Zhi-jun Z, Jian-hua L, Yi-fan W, Dong-xu C, Li-sheng L. Optimization of Pectin Extraction from Citrus Peel by Response Surgace Methodology. Food Science. 2011;18(18):128-32.
[24] Zhang C, Pang F, Li B, Xue S, Kang Y. Recycled aqueous ammonia expansion (RAAE) pretreatment to improve enzymatic digestibility of corn stalks. Bioresource technology. 2013 Jun 1;138:314-20.
[25] Tian SQ, Wang ZY, Fan ZL, Zuo LL. Optimization of CO2 laser-based pretreatment of corn stover using response surface methodology. Bioresource technology. 2011 Nov 1;102(22):10493-7.
[26] Teymouri F, Laureano-Pérez L, Alizadeh H, Dale BE. Ammonia fiber explosion treatment of corn stover. InProceedings of the Twenty-Fifth Symposium on Biotechnology for Fuels and Chemicals Held May 4–7, 2003, in Breckenridge, CO 2004 (pp. 951-963). Humana Press, Totowa, NJ.
[27] Zhang J, Lin G, Yin X, Zeng J, Wen S, Lan Y. Application of artificial neural network (ANN) and response surface methodology (RSM) for modeling and optimization of the contact angle of rice leaf surfaces. Acta Physiologiae Plantarum. 2020 Apr;42(4):1-5.