Nonlinear system identification based on ANFIS-Hammerstein model using Gravitational search algorithm

Fuzzy modeling and prediction of combustion layers′ temperature for power plant

This paper develops a fuzzy modeling strategy to study the temperature of different combustion layers in a power plant. First, a new infrared temperature measurement system is developed to measure three layers (bottom, middle and upper) temperature on both sides of the boiler. Then, a fuzzy clustering modeling algorithm is designed based on entropy to determine the structure of the fuzzy model and the corresponding fuzzy memberships of local models. The effect of modeling mismatches are overcome by the use of online identification of parameters. Simulation results show that the effectiveness of the proposed method can be achieved for a 660 MW power plant.

Identification of the Thermoelectric Cooler Using Hybrid Multi-Verse Optimizer and Sine Cosine Algorithm Based Continuous-Time Hammerstein Model

This paper presents the identification of the ThermoElectric Cooler (TEC) plant using a hybrid method of Multi-Verse Optimizer with Sine Cosine Algorithm (hMVOSCA) based on continuous-time Hammerstein model. These modifications are mainly for escaping from local minima and for making the balance between exploration and exploitation. In the Hammerstein model identification a continuous-time linear system is used and the hMVOSCA based method is used to tune the coefficients of both the Hammerstein model subsystems (linear and nonlinear) such that the error between the estimated output and the actual output is reduced. The efficiency of the proposed method is evaluated based on the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon’s rank test. The experimental findings show that the hMVOSCA can produce a Hammerstein system that generates an estimated output like the actual TEC output. Moreover, the identified outputs also show that the hMVOSCA outperforms other popular metaheuristic algorithms.

Preface

The volume contains extended versions of selected papers, presented at the International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), held in Sofia, Bulgaria in 2019.

Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant variables by taking the stochastic nature of batch process parameters explicitly into consideration. To handle the problem of missing data and lack of historical batch data, a bagging approach is introduced to generate individual quality-relevant sub-datasets, which are used to construct the corresponding monitoring sub-models. For each model, stochastic programming is used to construct an optimal quality trajectory, which is regarded as the reference for online quality monitoring. Then, for each sub-model, a corresponding control limit is obtained by computing historical residuals between the actual output and the optimal trajectory. For online monitoring, the current sample is examined by all sub-models, and whether the monitoring statistic exceeds the control limits is recorded for further analysis. The final step is ensemble learning via Bayesian fusion strategy, which is under the probabilistic framework. The implementation and effectiveness of the developed methodology are demonstrated through two case studies, including a numerical example, and a simulated fed-batch penicillin fermentation process.