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Ehsan Salehi

Ehsan Salehi

Academic rank: Professor
ORCID: https://orcid.org/0000-0003-4409-1242
Education: PhD.
ScopusId: 25643697300
Faculty: Engineering
Address: Arak University
Phone: 086-32625020

Research

Title
Multivariate data-based optimization of membrane adsorption process for wastewater treatment: Multi-layer perceptron adaptive neural network versus adaptive neural fuzzy inference system
Type
JournalPaper
Keywords
Adaptive neural-fuzzy inference system Membrane adsorption Optimization Sensitivity analysis
Year
2020
Journal CHEMOSPHERE
DOI
Researchers Ehsan Salehi ، Mohammad Khajavian ، Vahid Vatanpour ، Mika Sillanpaa ، Seyyed Ahmad Naghibi

Abstract

Application of machine-learning methods to assess the batch adsorption of malachite green (MG) dye on chitosan/polyvinyl alcohol/zeolite imidazolate frameworks membrane adsorbents (CPZ) was investigated in this study. Our previous research results proved the suitability of the CPZ membranes for wastewater decoloring. In the current work, the residence time was combined with the other operational variables i.e., pH, initial dye concentration, and adsorbent dose (AD), to obtain the possible interactions involved in nonequilibrium adsorption. Two well-known soft-computing approaches, multi-layer perceptron adaptive neural network (MLP-ANN) and adaptive neural fuzzy inference system (ANFIS), were selected among different machine learning alternatives and then, comprehensively compared with each other considering reliability and accuracy for a 60 number of runs. The ANFIS structure with nine centers of clusters could predict the adsorption performance better than the ANN approach. Root mean square error (RMSE) and R-square were obtained 0.01822 and 0.9958 for the test data, respectively. The interpretability test resulted a linear trend predicted by the model and disclosed that the maximum value of the removal efficiency (99.5%) could be obtained when the amount of the inputs set to the upper limit. Lastly, the sensitivity analysis uncovered that the residence time has a decisive effect (relevancy factor > 80%) on the removal efficiency. According to the results, ANFIS is an effective and reliable tool to optimize and intensify the membrane adsorption process.