Influence of number of membership functions on prediction of membrane systems using adaptive network based fuzzy inference system (ANFIS)

Abstract

In membrane separation technologies, membrane modules are used to separate chemical components. In membrane technology, understanding the behavior of fluids inside membrane module is challenging, and numerical methods are possible by using computational fluid dynamics (CFD). On the other hand, the optimization of membrane technology via CFD needs time and computational costs. Artificial Intelligence (AI) and CFD together can model a chemical process, including membrane technology and phase separation. This process can learn the process by learning the neural networks, and point by point learning of CFD mesh elements (computing nodes), and the fuzzy logic system can predict this process. In the current study, the adaptive neuro-fuzzy inference system (ANFIS) model and different parameters of ANFIS for learning a process based on membrane technology was used. The purpose behind using this model is to see how different tuning parameters of the ANFIS model can be used for increasing the exactness of the AI model and prediction of the membrane technology. These parameters were changed in this study, and the accuracy of the prediction was investigated. The results indicated that with low number of inputs, poor regression was obtained, less than 0.32 (R-value), but by increasing the number of inputs, the AI algorithm led to an increase in the prediction capability of the model. When the number of inputs increased to 4, the R-value was increased to 0.99, showing the high accuracy of model as well as its high capability in prediction of membrane process. The AI results were in good agreement with the CFD results. AI results were achieved in a limited time and with low computational costs. In terms of the categorization of CFD data-set, the AI framework plays a critical role in storing data in short memory, and the recovery mechanism can be very easy for users. Furthermore, the results were compared with Particle Swarm Optimization (PSOFIS), and Genetic Algorithm (GAFIS). The time for prediction and learning were compared to study the capability of the methods in prediction and their accuracy.

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Recent Advances in the Prediction of Fouling in Membrane Bioreactors

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Data-Driven AI-Based Parameters Tuning Using Grid Partition Algorithm for Predicting Climatic Effect on Epidemic Diseases

“…Tuning these parameters yields a complex fuzzy inference system (FIS), however, initializing such a system is troublesome. To design such FIS, a data-driven technique is a good choice to learn rules and tune FIS parameters [43]. Furthermore, During network training, our optimization algorithm generates candidate Sugeno FIS parameter sets.…”

“…For this goal, we tune FIS using Sugeno due to it's demonstrated (a) A Grid Partition of 5-inputs parameters with its generated membership rules (b) Surface view of extracted 2-input parameters partition a few numbers of output membership function parameters and faster defuzzification. However, FIS that accommodate large dataset generally has fast convergence with Sugeno FIS than Mamdani FIS [43]. Few numbers of membership functions and rules decrease the number of tuning parameters and yields fast tuning.…”