Lakhasly

Abstract: This paper presents an alternative method for predicting biochar yields from binmass thermochem- ical processes.The obtained results e compared to those of other methuda, such as ANFIS using gradient descent, practical swarm optimiza tion, genetic algorithm, whale optimization algorithm, sine cosine algorithm, and LS-SVM.There fore, this paper avoids this limitation by using a hybrid method between the adaptive neum-fuzzy inference system (ANFIS) and gray wolf optimization (GWO) algorithm.The aim of the second experiment was to evaluate the performance of the pro- posed ANFIS-GWO method to predict hiochar yield from manure pyrolysis.Keywords: Gray wolf optimization (GW)), adaptive neum-fuzzy inference system (ANFIS), renewable energy production, biochar prediction.Several methods have heen 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 binmass thermochem- ical processes. As biochar is considered a renewable and sustainable energy source, it has received more attention. Several methods have heen 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. There fore, this paper avoids this limitation by using a hybrid method between the adaptive neum-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 train ing 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) henchmark datasets to evaluate the effectiveness of ANFIS-GWD. The aim of the second experiment was to evaluate the performance of the pro- posed ANFIS-GWO method to predict hiochar yield from manure pyrolysis. The third experiment aimed to estimate the values of Finput input parameters parame of pyrolysis that maximize hiochar production. The obtained results e compared to those of other methuda, such as ANFIS using gradient descent, practical swarm optimiza tion, 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 (GW)), adaptive neum-fuzzy inference system (ANFIS), renewable energy production, biochar prediction.