Machine learning approaches to modeling and optimization of biodiesel production systems: State of art and future outlook

• Published in: Energy conversion and management. X Vol. 23; p. 100669
• Main Authors: Ishola, Niyi B., Epelle, Emmanuel I., Betiku, Eriola
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2024; Elsevier
• Subjects: Biodiesel; Computation; Esterification; Global optimization; Machine learning tools; Modeling; Nature-inspired algorithm; Optimization; Simulation; Soft computing; Transesterification; FFA; UCO; DE; LM; ETR; BBD; LR; ELM; GPR; HBC; NuSVR; GBDT; MRFO; SCSO; MDA; CCRD; DBM; QDA; DT; MLP; BPNN; DBN; RNN; UCOME; Machine learning tools; LASSO; MIMO; AIC; ANFIS; ML; R2; NSE; AAD; AI; MAPE; PSO; RSM; Biodiesel; WIA; PCA; MAE; XGB; FAME; RF; GAN; PKO; Computation; ADFFN; FAEE; PFAD; FAAE; SEP; RR; ANN; CNN; LDA; KNN; KRR; SVM; Modeling; SVR; Optimization; ABR; R; GBM; ANOVA; GBR; GA; Nature-inspired algorithm; MISO; GWO; MSE; CCD; EOR; HAC; RMSE; Transesterification; Soft computing; ACO; Esterification; Adjusted R2; CS; MOR; Global optimization; RBFNN; Simulation; MARS; KELM; DoE; NSGA; MRPD; CatBoost; FCCD
• ISSN: 2590-1745; 2590-1745
• DOI: 10.1016/j.ecmx.2024.100669

[Display omitted] •A comprehensive review of machine learning tools for modeling biodiesel synthesis.•Review on machine learning optimization techniques for modeling biodiesel synthesis.•Detailed description of ANN and ANFIS application in biodiesel synthesis.•Overview of emerging machine learning tools in biodiesel research.•Future outlook on machine learning application in biodiesel research. One of the main limitations to the economic sustainability of biodiesel production remains the high feedstock cost. Modeling and optimization are crucial steps to determine if processes (esterification and transesterification) involved in biodiesel production are economically viable. Phenomenological or mechanistic models can simulate the processes. These methods have been used to simulate and manage the processes, but their broad use has been constrained by computational complexity and numerical difficulties. Therefore, it is necessary to use quick, effective, accurate, and resilient modeling methodologies to simulate and regulate such complex systems. Data-driven computational and machine-learning (ML) techniques offer a potential replacement for conventional modeling methodologies to deal with the nonlinear, unpredictable, complex, and multivariate nature of biodiesel systems. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) are the most often utilized ML tools in biodiesel research. To effectively attain maximum biodiesel yield, suitable optimization techniques based on nature-inspired optimization algorithms need to be integrated with these tools to obtain the best possible combination of various operating variables. Future research should focus on utilizing ML approaches for monitoring and managing biodiesel production systems to increase their effectiveness and promote commercial feasibility. Thus, the review discusses the various ML techniques used in modeling and optimizing biodiesel production systems.