Improved Artificial Neural Network Training Based on Response Surface Methodology for Membrane Flux Prediction

Abstract

This paper presents an improved artificial neural network (ANN) training using response surface methodology (RSM) optimization for membrane flux prediction. The improved ANN utilizes the design of experiment (DoE) technique to determine the neural network parameters. The technique has the advantage of training performance, with a reduced training time and number of repetitions in achieving good model prediction for the permeate flux of palm oil mill effluent. The conventional training process is performed by the trial-and-error method, which is time consuming. In this work, Levenberg–Marquardt (lm) and gradient descent with momentum (gdm) training functions are used, the feed-forward neural network (FFNN) structure is applied to predict the permeate flux, and airflow and transmembrane pressure are the input variables. The network parameters include the number of neurons, the learning rate, the momentum, the epoch, and the training functions. To realize the effectiveness of the DoE strategy, central composite design is incorporated into neural network methodology to achieve both good model accuracy and improved training performance. The simulation results show an improvement of more than 50% of training performance, with less repetition of the training process for the RSM-based FFNN (FFNN-RSM) compared with the conventional-based FFNN (FFNN-lm and FFNN-gdm). In addition, a good accuracy of the models is achieved, with a smaller generalization error.

An AI-Extended Prediction of Erosion-Corrosion Degradation of API 5L X65 Steel

“…The conventional linear regression for multi-factorial analysis (MFA) and response surface method (RSM) has been primarily applied to the study of E-C phenomena, but re-cent studies have demonstrated the potential of Artificial Intelligence (AI)-based prediction methods, particularly Artificial Neural Networks (ANN), in producing better predictions in terms of statistical accuracy [26][27][28][29][30]. This method has gained popularity in various research areas with applications such as financial analysis, logistics management, weather predictions, etc., showcasing successful outcomes [31] with a substantial number of variables.…”

Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental-based AI modeling approach

“…Before the commencement of training, bias and weight values are assigned randomly[20][21][22][23][24].These values are then iteratively updated by the TrainLM function, which employs the gradient descent method to optimize the network. The training of the multilayer perceptron (MLP) model adheres to specifi c stopping criteria, namely a minimum gradient of 10-7 and a maximum of 10,000 epochs[25][26][27][28][29][30]. The ideal hyper-parameters for the ANN model are identifi ed in TableA1.…”