Soft computing modeling and multiresponse optimization for production of microalgal biomass and lipid as bioenergy feedstock

Abstract Microalga biomass is a reliable bioenergy feedstock to produce green fuel owing to its high lipid and organic content. On the other hand, the microalgal biomass productivity as well as lipid accumulation widely depends on various cultivation factors - including nitrogen/phosphorus ratio and light-dark cycles (LD). This study investigated the effects of LD and NaNO3 (nitrogen) dose on the specific growth rate (SGR), biomass productivity (P), and intracellular lipid productivity (LP) of Chlorella kessleri. Response surface methodology (RSM) and support vector regression (SVR) based nonlinear empirical models were developed to forecast SGR, P, and LP. The laboratory data acquired based on central composite design (CCD) matrix, was utilized to establish the adequacy of the models. Bayesian optimization algorithm (BOA) was coupled with SVR to tune the hyperparameters automatically. The performance of the hybrid intelligence model (BOA-SVR) was better than RSM model for anticipating all the responses. Lastly, the crow search algorithm was combined with BOA-SVR to achieve the global optimal solution for maximizing SGR, P and LP, simultaneously. The maximum SGR, P, and LP were found to be 0.302 d−1, 45.31 mgL−1d−1, and 16.3 mgL−1d−1, respectively at the operating environments of LD of 12/12 (h/h) and NaNO3 dose of 10.92 gL-1.

[1]  M. Park,et al.  Rapid quantification of microalgal lipids in aqueous medium by a simple colorimetric method. , 2014, Bioresource technology.

[2]  H. Sederoff,et al.  Interaction of Temperature and Photoperiod Increases Growth and Oil Content in the Marine Microalgae Dunaliella viridis , 2015, PloS one.

[3]  Ceyhun Yılmaz,et al.  Thermoeconomic modeling and artificial neural network optimization of Afyon geothermal power plant , 2021 .

[4]  S. Hossain,et al.  Experimental Study and Modeling Approach of Response Surface Methodology Coupled with Crow Search Algorithm for Optimizing the Extraction Conditions of Papaya Seed Waste Oil , 2020 .

[5]  Chin-Chao Chen,et al.  Lipid accumulating microalgae cultivation in textile wastewater: Environmental parameters optimization , 2017 .

[6]  Junbin Gao,et al.  A Probabilistic Framework for SVM Regression and Error Bar Estimation , 2002, Machine Learning.

[7]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[8]  W. J. Dyer,et al.  A rapid method of total lipid extraction and purification. , 1959, Canadian journal of biochemistry and physiology.

[9]  J. Costa,et al.  Isolation and selection of microalgae from coal fired thermoelectric power plant for biofixation of carbon dioxide , 2007 .

[10]  Jyeshtharaj B. Joshi,et al.  Estimation of heat transfer coefficient in bubble column reactors using support vector regression , 2010 .

[11]  S. Arumugam,et al.  RSM and Crow Search Algorithm-Based Optimization of Ultrasonicated Transesterification Process Parameters on Synthesis of Polyol Ester-Based Biolubricant , 2019, Arabian Journal for Science and Engineering.

[12]  N. Ren,et al.  Cell growth and lipid accumulation of a microalgal mutant Scenedesmus sp. Z-4 by combining light/dark cycle with temperature variation , 2017, Biotechnology for Biofuels.

[13]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[14]  S. Hossain,et al.  Modeling and optimization of non-edible papaya seed waste oil synthesis using data mining approaches , 2020 .

[15]  Pengcheng Wei,et al.  Enhanced support vector regression based forecast engine to predict solar power output , 2018, Renewable Energy.

[16]  N. Sato,et al.  Regulatory carbon metabolism underlying seawater-based promotion of triacylglycerol accumulation in Chlorella kessleri. , 2019, Bioresource technology.

[17]  Cong Ma,et al.  Operation parameters optimization of a hybrid dead-end/cross-flow forward osmosis system for microalgae dewatering by response surface methodology , 2020 .

[18]  Wang Guohua,et al.  Prediction Model of Alga's Growth Based on Support Vector Regression , 2009, 2009 International Conference on Environmental Science and Information Application Technology.

[19]  Hayatullahi B. Adeyemo,et al.  Modeling of autoignition temperature of organic energetic compounds using hybrid intelligent method , 2018, Process Safety and Environmental Protection.

[20]  A. Idris,et al.  The influence of light intensity and photoperiod on the growth and lipid content of microalgae Nannochloropsis sp. , 2013, Bioresource technology.

[21]  S. Razzak,et al.  Application of Central Composite Design to Optimize Culture Conditions of Chlorella vulgaris in a Batch Photobioreactor: An Efficient Modeling Approach , 2018 .

[22]  F. Nan,et al.  Physiological Changes of Parachlorella Kessleri TY02 in Lipid Accumulation under Nitrogen Stress , 2019, International journal of environmental research and public health.

[23]  Joo-Hwee Lim,et al.  Detection of Pathological Myopia by PAMELA with Texture-Based Features through an SVM Approach , 2010 .

[24]  Y. Wong,et al.  Effect of ammonia concentrations on growth of Chlorella vulgaris and nitrogen removal from media , 1996 .

[25]  Shantanu Roy,et al.  Machine learning based position‐rendering algorithms for radioactive particle tracking experimentation , 2020 .

[26]  Rajendra Kumar Sharma,et al.  Artificial Neural Networks for the Prediction of Compressive Strength of Concrete , 2015 .

[27]  Effects of CO2 Concentration and pH on Mixotrophic Growth of Nannochloropsis oculata , 2015, Applied Biochemistry and Biotechnology.

[28]  Aboul Ella Hassanien,et al.  Feature selection via a novel chaotic crow search algorithm , 2017, Neural Computing and Applications.

[29]  S. Razzak,et al.  Biological CO2 fixation with production of microalgae in wastewater – A review , 2017 .

[30]  Sunday O. Olatunji,et al.  Development and validation of surface energies estimator (SEE) using computational intelligence technique , 2015 .

[31]  Jun Cheng,et al.  Enhancing lipid production in microalgae Chlorella PY-ZU1 with phosphorus excess and nitrogen starvation under 15% CO2 in a continuous two-step cultivation process , 2019, Chemical Engineering Journal.

[33]  P. Show,et al.  The effect of stress environment towards lipid accumulation in microalgae after harvesting , 2020 .

[34]  J. Costa,et al.  Growth stimulation and synthesis of lipids, pigments and antioxidants with magnetic fields in Chlorella kessleri cultivations. , 2017, Bioresource technology.

[35]  Bo Tang,et al.  Numerical study on the relationship between high sharpness and configurations of the vortex finder of a hydrocyclone by central composite design , 2015 .

[36]  Choul‐Gyun Lee,et al.  Effect of light/dark cycles on wastewater treatments by microalgae , 2001 .

[37]  Z. Ahmad,et al.  Hybrid Intelligent Modelling of the Viscoelastic Moduli of Coal Fly Ash Based Polymer Gel System for Water Shutoff Treatment in Oil and Gas Wells , 2019, The Canadian Journal of Chemical Engineering.

[38]  P. Caboni,et al.  A novel investigation of the growth and lipid production of the extremophile microalga Coccomyxa melkonianii SCCA 048 under the effect of different cultivation conditions: Experiments and modeling , 2019 .

[39]  S. Revah,et al.  Effect of the temperature, pH and irradiance on the photosynthetic activity by Scenedesmus obtusiusculus under nitrogen replete and deplete conditions. , 2015, Bioresource technology.

[40]  H. Jeong,et al.  Easy and rapid quantification of lipid contents of marine dinoflagellates using the sulpho-phospho-vanillin method , 2016 .

[41]  Shane T. Grosser,et al.  Stoichiometry identification of pharmaceutical reactions using the constrained dynamic response surface methodology , 2019, AIChE Journal.

[42]  J. Perales,et al.  Freshwater microalgae selection for simultaneous wastewater nutrient removal and lipid production , 2017 .

[43]  Nayan Shrestha,et al.  Effects of nitrogen and phosphorus limitation on lipid accumulation by Chlorella kessleri str. UTEX 263 grown in darkness , 2020, Journal of Applied Phycology.

[44]  Charles D. Griego,et al.  Machine learning corrected alchemical perturbation density functional theory for catalysis applications , 2020 .

[45]  W. Djoudi,et al.  Optimization of copper cementation process by iron using central composite design experiments , 2007 .

[46]  Mohammad S. Islam,et al.  Soft computing approaches for comparative prediction of the mechanical properties of jute fiber reinforced concrete , 2020, Adv. Eng. Softw..

[47]  S. Zaidi,et al.  Development of SVR-based model and comparative analysis with MLR and ANN models for predicting the sorption capacity of Cr(VI) , 2017 .

[48]  S. Hossain,et al.  Multiobjective optimization of microalgae (Chlorella sp.) growth in a photobioreactor using Box‐Behnken design approach , 2018 .

[49]  Sunday O. Olatunji,et al.  Modeling thermal conductivity enhancement of metal and metallic oxide nanofluids using support vector regression , 2018 .

[50]  Mohd Amiruddin Abd Rahman,et al.  Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm - support vector regression model , 2018, Comput. Methods Programs Biomed..

[51]  Paresh Chandra Deka,et al.  Support vector machine applications in the field of hydrology: A review , 2014, Appl. Soft Comput..

[52]  Diptendu Sinha Roy,et al.  Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization , 2017, Journal of healthcare engineering.

[53]  Yu-Han Chang,et al.  Engineering strategies for enhancing C. vulgaris ESP-31 lipid production using effluents of coke-making wastewater. , 2018, Journal of bioscience and bioengineering.

[54]  S. Razzak,et al.  Integrated CO2 capture, wastewater treatment and biofuel production by microalgae culturing—A review , 2013 .

[55]  Accumulation of Lipids and Triglycerides in Isochrysis galbana Under Nutrient Stress. , 2019 .

[56]  Md Shah Alam,et al.  An experimental investigation and modeling approach of response surface methodology coupled with crow search algorithm for optimizing the properties of jute fiber reinforced concrete , 2020 .

[57]  G. Bennett,et al.  Statistical evaluation of Lower Flammability Distance (LFD) using four hazardous release models , 1993 .

[58]  Jamal Alhiyafi,et al.  Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study , 2019, Comput. Biol. Medicine.

[59]  G. S. Rolim,et al.  Influence of light intensity and tannery wastewater concentration on biomass production and nutrient removal by microalgae Scenedesmus sp. , 2017 .

[60]  Lipid accumulation in response to nitrogen limitation and variation of temperature in Nannochloropsis salina , 2015, Botanical Studies.

[61]  Taoreed O. Owolabi,et al.  Novel techniques for enhancing the performance of support vector regression chemo-metric in quantitative analysis of LIBS spectra , 2017 .

[62]  Yanjie Wang,et al.  Algal bloom prediction by support vector machine and relevance vector machine with genetic algorithm optimization in freshwater reservoirs , 2017 .

[63]  Mayur B. Kurade,et al.  Recent progress in microalgal biomass production coupled with wastewater treatment for biofuel generation , 2017 .

[64]  Sunday Olusanya Olatunji,et al.  Estimation of surface energies of hexagonal close packed metals using computational intelligence technique , 2015, Appl. Soft Comput..

[65]  N. K. Bellam,et al.  Performance of an industrial source complex model: Predicting long‐term concentrations in an urban area , 1999 .

[66]  D. Kvasov,et al.  Lipschitz optimization methods for fitting a sum of damped sinusoids to a series of observations , 2017 .

[67]  D. Bilanović,et al.  Freshwater and marine microalgae sequestering of CO2 at different C and N concentrations – Response surface methodology analysis , 2009 .

[68]  C. Barrow,et al.  A quick colorimetric method for total lipid quantification in microalgae. , 2016, Journal of microbiological methods.

[69]  Ben Niu,et al.  Multi-objective bacterial foraging optimization , 2013, Neurocomputing.

[70]  V. Prasad,et al.  Optimization of CO2 fixation by Chlorella kessleri using response surface methodology , 2015 .