Enhanced lignin extraction and optimisation from oil palm biomass using neural network modelling

Abstract Lignin from industrial crops is the most promising feedstock which can be used to function modern industrial societies. However, it is very challenging to separate lignin from lignocellulosic biomass effectively. Commercial application of lignin faces many challenges concerning practical applications and sub-optimal extraction approaches. Investigating one factor at a time is a significant limitation in standard experimental protocols. The current processing conditions need to be improved, which can be performed by modelling the processing conditions and identifying the most appropriate process conditions to suit the market demands. In this study, both the response surface methodology (RSM) and an artificial neural network (ANN) model was developed for the enhanced lignin extraction from the available experimental data of our previous work. The effect of various operating parameters such as; extraction temperature, time, particle size range and solid loading affecting the lignin extraction efficiency was optimally analyzed. Likewise, this is the first study reporting a detailed comparison and prediction of lignin extraction using RSM and ANN. The models were evaluated through the coefficient of determination (R2), Root Means Square Error (RMSE) Mean Average Deviation (MAD) and Average Absolute Relative Error (AARE) showing that the ANN was superior (R2 = 0.9933, RMSE = 1.129) to the RSM model (R2 = 0.8805, RMSE = 4.784) for lignin extraction efficiency predictions using various species of oil palm biomass. The results showed the accuracy of the ANN model in the prediction of lignin extraction from empty fruit bunches (EFB), palm mesocarp fibre (PMF) and palm kernel shells (PKS), as compared to the RSM model.

[1]  O. Onukwuli,et al.  Optimization of methyl ester production from Prunus Amygdalus seed oil using response surface methodology and Artificial Neural Networks , 2019, Renewable Energy.

[2]  Shaotong Jiang,et al.  Optimization of Molecular Distillation for Recovery of Tocopherol from Rapeseed Oil Deodorizer Distillate Using Response Surface and Artificial Neural Network Models , 2007 .

[3]  J. Klemeš,et al.  Torrefied biomass fuels as a renewable alternative to coal in co-firing for power generation , 2020, Energy.

[4]  G. Trystram,et al.  Dynamic modeling of crossflow microfiltration using neural networks , 1995 .

[5]  Tazien Rashid,et al.  Effect of alkyl chain length on the thermophysical properties of pyridinium carboxylates , 2017 .

[6]  L. Marczak,et al.  Evaluation of water, sucrose and NaCl effective diffusivities during osmotic dehydration of banana (Musa sapientum, shum.) , 2011 .

[7]  Annan Zhou,et al.  Modelling of municipal solid waste gasification using an optimised ensemble soft computing model , 2021 .

[8]  I. Ali,et al.  Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks , 2018, Fuel.

[9]  Tazien Rashid,et al.  Enhanced lignin extraction from different species of oil palm biomass: Kinetics and optimization of extraction conditions , 2018, Industrial Crops and Products.

[10]  F. Bamoharram,et al.  Comparison of RSM and ANN for the investigation of linear alkylbenzene synthesis over H14[NaP5W30O110]/SiO2 catalyst , 2013 .

[11]  Robert Mikulandrić,et al.  Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network , 2020 .

[12]  J. Saddler,et al.  Organosolv ethanol lignin from hybrid poplar as a radical scavenger: relationship between lignin structure, extraction conditions, and antioxidant activity. , 2006, Journal of agricultural and food chemistry.

[13]  J. Maran,et al.  Ultrasound assisted extraction of bioactive compounds from Nephelium lappaceum L. fruit peel using central composite face centered response surface design , 2017 .

[14]  G. Huber,et al.  Catalytic Transformation of Lignin for the Production of Chemicals and Fuels. , 2015, Chemical reviews.

[15]  Jun He,et al.  Optimization of dark fermentation for biohydrogen production using a hybrid artificial neural network (ANN) and response surface methodology (RSM) approach , 2020, Environmental Progress & Sustainable Energy.

[16]  Tareq Al-Ansari,et al.  Air catalytic biomass (PKS) gasification in a fixed-bed downdraft gasifier using waste bottom ash as catalyst with NARX neural network modelling , 2020, Comput. Chem. Eng..

[17]  K. Natarajan,et al.  Modeling and optimization of tannase production with Triphala in packed bed reactor by response surface methodology, genetic algorithm, and artificial neural network , 2019, 3 Biotech.

[18]  H. Ahmadian-Moghadam,et al.  Prediction of Ethanol Concentration in Biofuel Production Using Artificial Neural Networks , 2013 .

[19]  Qizhao Lin,et al.  Thermodynamics, kinetics, gas emissions and artificial neural network modeling of co-pyrolysis of sewage sludge and peanut shell , 2021 .

[20]  S. Soni,et al.  Optimization of Valorization of Biodegradable Kitchen Waste Biomass for Production of Fungal Cellulase System by Statistical Modeling , 2014 .

[21]  H. Arafat,et al.  Optimization of lignin recovery from sugarcane bagasse using ionic liquid aided pretreatment , 2017, Cellulose.

[22]  Abrar Inayat,et al.  Artificial neural network approach for the steam gasification of palm oil waste using bottom ash and CaO , 2019, Renewable Energy.

[23]  Ye Sun,et al.  Hydrolysis of lignocellulosic materials for ethanol production: a review. , 2002, Bioresource technology.

[24]  A. Corma,et al.  Synthesis of transportation fuels from biomass: chemistry, catalysts, and engineering. , 2006, Chemical reviews.

[25]  Jyri-Pekka Mikkola,et al.  Dissolution of lignocellulosic materials and its constituents using ionic liquids - a review , 2010 .

[26]  Chennakesava R Alavala,et al.  Fuzzy logic and neural networks : basic concepts & application , 2008 .

[27]  Joong-Ho Kwon,et al.  Optimization of microwave-assisted extraction of total extract, stevioside and rebaudioside-A from Stevia rebaudiana (Bertoni) leaves, using response surface methodology (RSM) and artificial neural network (ANN) modelling. , 2017, Food chemistry.

[28]  Mohammad Amin Sobati,et al.  Optimization of a continuous ultrasound assisted oxidative desulfurization (UAOD) process of diesel using response surface methodology (RSM) considering operating cost , 2020 .

[29]  John D. Floros,et al.  Optimization of osmotic dehydration of diced green peppers by response surface methodology , 2008 .

[30]  J. Melero,et al.  Biomass as renewable feedstock in standard refinery units. Feasibility, opportunities and challenges , 2012 .

[31]  Yun Ji,et al.  Recent Development in Chemical Depolymerization of Lignin: A Review , 2013 .

[32]  N. Ngadi,et al.  Optimization of lignin production from empty fruit bunch via liquefaction with ionic liquid. , 2013, Bioresource technology.

[33]  Hafiz M.N. Iqbal,et al.  Effect of protic ionic liquid treatment on the pyrolysis products of lignin extracted from oil palm biomass , 2021 .

[34]  Kenneth N. Marsh,et al.  Extracting wood lignin without dissolving or degrading cellulose: investigations on the use of food additive-derived ionic liquids , 2011 .

[35]  S. Uslu,et al.  Performance and emission prediction of a compression ignition engine fueled with biodiesel-diesel blends: A combined application of ANN and RSM based optimization , 2020 .

[36]  Deborah F. Cook,et al.  Evaluation of neural network variable influence measures for process control , 2011, Eng. Appl. Artif. Intell..

[37]  A. Maulud,et al.  Artificial Neural Network for Anomalies Detection in Distillation Column , 2017, AsiaSim 2017.

[38]  A. Vaidya,et al.  Artificial Neural Network Modeling in Pretreatment of Garden Biomass for Lignocellulose Degradation , 2019 .

[39]  Z. Zhijun,et al.  Optimization of ultrasound-assisted hexane extraction of perilla oil using response surface methodology , 2015 .

[40]  J. Kennedy,et al.  Application of response surface methodology for optimization of polysaccharides production parameters from the roots of Codonopsis pilosula by a central composite design , 2010 .

[41]  George M. Bollas,et al.  Using hybrid neural networks in scaling up an FCC model from a pilot plant to an industrial unit , 2003 .

[42]  Y. Teoh,et al.  An Experimental Investigation on Tribological Behaviour of Tire-Derived Pyrolysis Oil Blended with Biodiesel Fuel , 2020, Sustainability.

[43]  N. Muhammad,et al.  Investigations of novel nitrile-based ionic liquids as pre-treatment solvent for extraction of lignin from bamboo biomass , 2013 .

[44]  H. R. Ghatak,et al.  Process optimization of lignin conversion into value added chemicals by thermochemical pretreatment and electrooxidation on a stainless steel anode , 2018 .

[45]  Patrícia Moreira Azoubel,et al.  Influence of the osmotic agent on the osmotic dehydration of papaya (Carica papaya L.) , 2006 .

[46]  A. Mohamed,et al.  Pretreatment of lignocellulosic palm biomass using a solvent-ionic liquid [BMIM]Cl for glucose recovery: an optimisation study using response surface methodology. , 2011 .

[47]  Giuseppe Mazza,et al.  Optimization of processing conditions for the pretreatment of wheat straw using aqueous ionic liquid. , 2011, Bioresource technology.

[48]  M. Rodrigues,et al.  Response surface analysis and simulation as a tool for bioprocess design and optimization , 2000 .

[49]  D. Gokhale,et al.  Development of biocatalysts for production of commodity chemicals from lignocellulosic biomass. , 2011, Bioresource technology.

[50]  X. Zou,et al.  Optimization of extraction process by response surface methodology and preliminary characterization of polysaccharides from Phellinus igniarius , 2010 .

[51]  Yung-Chuan Liu,et al.  RSM and ANN modeling-based optimization approach for the development of ultrasound-assisted liposome encapsulation of piceid. , 2017, Ultrasonics sonochemistry.

[52]  Mahiran Basri,et al.  Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester , 2007, BMC biotechnology.

[53]  S. Kavitha,et al.  Optimization of Process Parameters for Efficient Bioconversion of Thermo-chemo Pretreated Manihot esculenta Crantz YTP1 Stem to Ethanol , 2019 .

[54]  Rekha S. Singhal,et al.  Use of an artificial neural network in modeling yeast biomass and yield of β-glucan , 2005 .