Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

Abstract

Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass by using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE), and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10-10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

Comparative evaluation of AI ‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe 3 O 4 ‐coated MWCNT hybrid nanofluids for potential application in energy systems

“…A Spearman rank correlation can be employed to measure monotonic association for nonnormally distributed continuous data, ordinal data, or data with noteworthy outliers. Both correlation coefficients are scaled from −1 to +1, with 0 indicating no linear or monotonic association and 1 indicating a greater relationship that eventually approaches a straight line 51,65 …”

“…Unlike many optimization strategies, which need prior knowledge of the relationship between the model parameters and the output parameter. As a result, many optimization approaches' rigors in establishing the model parameter combination that will produce the best outcomes have been overcome 51,52 . GEP had been used effectively in the model prediction of complex engineering problems such as combustion‐emission modeling of biodiesel‐powered engines, 53,54 modeming of green concrete properties, 55 and model prediction of meteorological data 56 .…”

“…As a result, many optimization approaches' rigors in establishing the model parameter combination that will produce the best outcomes have been overcome. 51,52 GEP had been used effectively in the model prediction of complex engineering problems such as combustion-emission modeling of biodiesel-powered engines, 53,54 modeming of green concrete properties, 55 and model prediction of meteorological data. 56 Despite being a very effective prognostic tool, GEP's prediction potential has yet to be completely realized in the realm of hybrid nanofluid characterization.…”