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.
Highlights
Complex industrial processes (CIPs) [1] are often composed of closely-coupled sub-circuit processes, involving a series of strongly-coupled equipment
In order to further evaluate the proposed state transition algorithm (STA)-adaptive presparse neuro-fuzzy inference system (APSNFIS), we carried out experiments comparing the concentrate grade (A/S of cleaner II) prediction results with that of the soft sensor methods based on adaptive neuro-fuzzy inference system (ANFIS), particle swarm optimization (PSO)-ANFIS, genetic algorithms (GA)-ANFIS, LS-support vector machines (SVM), TSA-BP and STA-APSNFIS in this
This paper proposes an end-to-end soft-measurement method based on the STA-optimized adaptive pre-sparse neural-fuzzy inference system for online key production indicators (KPIs) monitoring of CIPs, termed STA-APSNFIS
Summary
Complex industrial processes (CIPs) [1] are often composed of closely-coupled sub-circuit processes, involving a series of strongly-coupled equipment. Despite the strong learning power of ANNs and explicit knowledge representation of FISs, traditional ANFIS-based soft sensor is still intractable to fit complex nonlinear relations in a CIP effectively due to the highdimensional, redundant, and strong noise-interfering parameters in the CIP. It will generate a very large network structure that consumes a lot of time and memory space, making it difficult to meet the requirements of the online KPI monitoring for the CIP monitoring.
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