A robust cutting pattern recognition method for shearer based on Least Square Support Vector Machine equipped with Chaos Modified Particle Swarm Optimization and Online Correcting Strategy.

Accurate cutting pattern recognition method for shearer in coal mining process has drawn more and more attention over the past decades due to its important role in guaranteeing the steady operation of the equipment, which, however, remains challenging caused by the mismatch of cutting pattern recognition especially for dynamic uncertainty of future sampled data. Therefore, a novel approach for cutting pattern recognition with an optimal Online Correcting Strategy (OCS) combined with Least Square Support Vector Machine (LSSVM) and Chaos Modified Particle Swarm Optimization (CMPSO) algorithm, named OCS-CMPSO-LSSVM, is proposed, where LSSVM models the functional relationship between input and output of the system, CMPSO optimizes the parameters of LSSVM, and OCS modifies the model to reduce its mismatch as the system runs, respectively. The performance of the proposed model is demonstrated with a simulation experiment and compared with the existing methods reported in the literature in detail. The experimental results reveal that the proposed models can achieve better cutting pattern recognition performance and higher robustness.

Production optimization for concentration and volume-limited fed-batch reactors in biochemical processes.

Since a very slight violation of constraint could cause process safety and product quality problems in biochemical processes, an adaptive approach of fed-batch reactor production optimization that can strictly satisfy constraints over the entire operating time is presented. In this approach, an improved smooth function is proposed such that the inequality constraints can be transformed into smooth constraints. Based on this, only an auxiliary state is needed to monitor violations in the augmented performance index. Combined with control variable parameterization (CVP), the dynamic optimization is executed and constraint violations are examined by calculating the sensitivities of states to ensure that the inequality constraints are satisfied everywhere inside the time interval. Three biochemical production optimization problems, including the manufacturing of ethanol, penicillin and protein, are tested as illustrations. Meanwhile, comparisons with pure penalty CVP method, famous dynamic optimization toolbox DOTcvp and literature results are carried out. Research results show that the proposed method achieves better performances in terms of optimization accuracy and computation cost.

Sensitivity-based adaptive mesh refinement collocation method for dynamic optimization of chemical and biochemical processes.

Collocation on finite element (CFE) is an effective simultaneous method of dynamic optimization to increase the profitability or productivity of industrial process. The approach needs to select an optimal mesh of time interval to balance the computational cost with desired solution. A new CFE approach with non-uniform refinement procedure based on the sensitivity analysis for dynamic optimization problems is, therefore, proposed, where a subinterval is further refined if the obtained control parameters have significant effect on the performance index. To improve the efficiency, the sensitivities of state parameters with respect to control parameters are derived from the solution of the discretized dynamic system. The proposed method is illustrated by testing two classic dynamic optimization problems from chemical and biochemical engineering. The detailed comparisons among the proposed method, the CFE with uniform mesh, and other reported methods are also carried out. The research results reveal the effectiveness of the proposed approach.

A new sensitivity-based adaptive control vector parameterization approach for dynamic optimization of bioprocesses.

Dynamic optimization is a very effective way to increase the profitability or productivity of bioprocesses. As an important method of dynamic optimization, the control vector parameterization (CVP) approach needs to select an optimal discretization level to balance the computational cost with the desired solution quality. A new sensitivity-based adaptive refinement method is therefore proposed, by which new time grid points are only inserted where necessary and unnecessary points are eliminated so as to obtain economic and effective discretization grids. Moreover, considering that traditional refinement methods may cost a lot to get the high-quality solutions of some bioprocess problems, whose performance indices are sensitive to some significant time points, an optimization technique is further proposed and embedded into the new sensitivity-based CVP approach to efficiently solve these problems. The proposed methods are applied to two well-known bioprocess optimization problems and the results illustrate their effectiveness.