Improved Estimation of Bio-Oil Yield Based on Pyrolysis Conditions and Biomass Compositions Using GA- and PSO-ANFIS Models.

This paper incorporates the adaptive neurofuzzy inference system (ANFIS) technique to model the yield of bio-oil. The estimation of this parameter was performed according to pyrolysis conditions and biomass compositions of feedstock. For this purpose, this paper innovates two optimization methods including a genetic algorithm (GA) and particle swarm optimization (PSO).

On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models.

This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters.

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food.

In this paper, the Trolox equivalent antioxidant capacity (TEAC) is estimated through a robust machine-learning algorithm known as the Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) model. For this purpose, a large dataset from previously published reports was gathered. Various analyses were performed to evaluate the proposed model.