Dexian Huang

TL;DR: Simulation results and comparisons with the standard PSO and several meta-heuristics show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality.

TL;DR: The comparison of modeling results demonstrates that the deep learning technique is especially suitable for soft sensor modeling because of the following advantages over traditional methods.

Abstract: Latent variable (LV) models have been widely used in multivariate statistical process monitoring. However, whatever deviation from nominal operating condition is detected, an alarm is triggered based on classical monitoring methods. Therefore, they fail to distinguish real faults incurring dynamics anomalies from normal deviations in operating conditions. A new process monitoring strategy based on slow feature analysis (SFA) is proposed for the concurrent monitoring of operating point deviations and process dynamics anomalies. Slow features as LVs are developed to describe slowly varying dynamics, yielding improved physical interpretation. In addition to classical statistics for monitoring deviation from design conditions, two novel indices are proposed to detect anomalies in process dynamics through the slowness of LVs. The proposed approach can distinguish whether the changes in operating conditions are normal or real faults occur. Two case studies show the validity of the SFA-based process monitoring approach. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3666–3682, 2015

TL;DR: An effective hybrid algorithm based on differential evolution (DE), namely HDE, is proposed to solve multi-objective permutation flow shop scheduling problem (MPFSSP) with limited buffers between consecutive machines, which is a typical NP-hard combinatorial optimization problem with strong engineering background.

TL;DR: Simulations and comparisons based on benchmarks are carried out, which show the effectiveness, efficiency, and robustness of the proposed HDE and MHDE for the single-objective PFSSPs.