Lakhasly

Abstract: This paper presents an alternative method for predicting biochar yields from biomass thermochemical processes.The obtained results were compared to those of other methods, such as ANFIS using gradient descent, practical swarm optimization, genetic algorithm, whale optimization algorithm, sine-cosine algorithm, and LS-SVM.Therefore, this paper avoids this limitation by using a hybrid method between the adaptive neuro-fuzzy inference system (ANFIS) and gray wolf optimization (GWO) algorithm.The aim of the second experiment was to evaluate the performance of the proposed ANFIS-GWO method to predict biochar yield from manure pyrolysis.Keywords: Gray wolf optimization (GWO), adaptive neuro-fuzzy inference system (ANFIS), renewable energy production, biochar predictionSeveral methods have been presented to predict biochar, such as neural network (NN) and least square support vector machine (LS-SVM).The proposed method is called ANFIS-GWO, which consists of two stages.

Abstract: This paper presents an alternative method for predicting biochar yields from biomass thermochemical processes. As biochar is considered a renewable and sustainable energy source, it has received more
attention. Several methods have been presented to predict biochar, such as neural network (NN) and least
square support vector machine (LS-SVM). However, each of them has its own drawbacks, such as getting stuck
in a local optimum, which occurs in NN, and lack of uncertainty and time complexity, as in LS-SVM. Therefore, this paper avoids this limitation by using a hybrid method between the adaptive neuro-fuzzy inference
system (ANFIS) and gray wolf optimization (GWO) algorithm. The proposed method is called ANFIS-GWO,
which consists of two stages. In the first stage, GWO is used to learn the parameters of ANFIS using the training set. Meanwhile, in the second stage, the testing set is used to evaluate the performance of the proposed
ANFIS-GWO method. Three experiments were performed to assess the performance of the proposed method.
The first experiment used a set of UCI (University of California, Irvine) benchmark datasets to evaluate the
effectiveness of ANFIS-GWO. The aim of the second experiment was to evaluate the performance of the proposed ANFIS-GWO method to predict biochar yield from manure pyrolysis. The third experiment aimed to
estimate the values of input parameters of pyrolysis that maximize biochar production. The obtained results
were compared to those of other methods, such as ANFIS using gradient descent, practical swarm optimization, genetic algorithm, whale optimization algorithm, sine-cosine algorithm, and LS-SVM. The results of the
ANFIS-GWO method were >35% of the standard ANFIS and also better than those of other methods.
Keywords: Gray wolf optimization (GWO), adaptive neuro-fuzzy inference system (ANFIS), renewable energy
production, biochar prediction