STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

Abstract

In complex industrial processes (CIPs), due to technical and economic limitations, key performance indicators (KPIs), especially the chemical content-related KPIs, are often difficult to measure in real time, which hinders the propagation of advanced process control technologies. This paper presents a soft sensor-based online KPI inference scheme by a state transition algorithm (STA)-optimized adaptive pre-sparse neuro-fuzzy inference system model, called STA-APSNFIS. It introduces a pre-sparse neural network to the traditional adaptive neuro-fuzzy inference system (ANFIS) model to establish an adaptive pre-sparse neuro-fuzzy inference system (APSNFIS) model to alleviate the adverse effects of data redundancy and noise interference in the detectable process monitoring data, which can effectively reduce the complexity of neuro-fuzzy inference system (NFIS) and speed up its convergence. Successively, to avoid being trapped at a local optimum, the STA-based optimization algorithm is adopted to replace the traditional gradient-based optimization approach to achieve an optimal APSNFIS model. Extensive validation and comparative experiments on nonlinear numeric simulation systems, benchmark Tenessee Eastman (TE) process and a real industrial bauxite flotation process demonstrated that the proposed STA-APSNFIS performed favorably against traditional ANFIS model as well as its variants, e.g., PSO-ANFIS, GA-ANFIS, and some other soft sensor-based KPI inference models.

A Design and Its Application of Multi-Granular Fuzzy Model with Hierarchical Tree Structures

“…However, NNs have difficulty understanding a system because they do not have information about the given system. A complementary model, called a neuro-fuzzy system, was proposed by combining the advantages of fuzzy models and neural networks to address the abovementioned issues [1][2][3][4][5]. Studies are actively being conducted on neuro-fuzzy inference systems [6][7][8][9][10][11][12][13][14][15].…”

Soft sensor of iron tailings grade based on froth image features for reverse flotation

“…This method is also called “soft sensor” (McCoy and Auret, 2019). Soft sensor technology has been widely used in the flotation process (Ai et al, 2020; Jian et al, 2020; Liu, Jiang, He, et al 2020; Zhang et al, 2020). The artificial neural network is commonly used in soft sensor flotation, followed by fuzzy logic, genetic algorithm, support vector machine, and learning decision tree.…”