Qiqiang Li

A Simulation Method of Controlled Hybrid Petri Nets Based on Matlab Simulink/Stateflow

Integrated with Matlab environment, Simulink and Stateflow are used to simulate complex systems and event- driven systems. In this paper, they are applied to simulate controlled hybrid Petri nets. The continuous parts and the discrete parts of controlled hybrid Petri nets are constructed separately by Simulink and Stateflow. Basic rules and steps are developed to construct a corresponding Simulink/Stateflow model from a given controlled hybrid Petri nets model. An example about modeling a typical controlled hybrid Petri nets is given, and the results show that the presented method is easily used, all- purpose and can be extended.

Improving monitoring accuracy of process based on SPC method

In order to improve the monitoring accuracy for the control variables in the production process, a new method to use statistical process control (SPC) was proposed, which analyzed the production process data by the SPC. It can extract the main features of the process variable, and set up the upper and lower monitoring limits for the production process. The decision-making basis for the operator can be provided by monitoring control accuracy of the production process. With this method, it can improve the control accuracy of the process variable and provide the information quickly and efficiently for the fault diagnosis of production process. An on-line monitoring system was designed for the distillation process as an example, and the effectiveness of this method was illustrated.

Robust multi-objective optimization for energy production scheduling in microgrids

ABSTRACT In order to achieve better economic and environmental benefits of microgrids (MGs) under multiple uncertainties in renewable energy resources and loads, a novel energy production scheduling method is proposed based on robust multi-objective optimization with minimax criterion. Firstly, a mixed integer minimax multi-objective formulation is developed to capture uncertainties as well as minimize economic and environmental objectives. Secondly, the primal problem is decomposed into a bi-level optimization problem, which attempts to seek robust scheduling scheme set under the worst-case realization of uncertainties in a multi-objective framework. Finally, a hierarchical meta-heuristic solution strategy, including multi-objective cross entropy algorithm and δ+ indicator, is designed to solve the reconstructed problem. Numerical results demonstrate that the proposed scheduling method can effectively attenuate the disturbance of uncertainties as well as reduce energy costs and emissions, as compared with single-objective robust optimization and multi-objective optimization scheduling approaches. This study could offer useful insights which help decision-makers balance robustness and comprehensive benefits in the operation of MGs.

Generalized disjunctive programming model for multi-periodic continuous process scheduling

In continuous production process, there are many complex equipments and special requirements such as continuity and stability, which are very difficult to be presented in classical MINLP formulations. So the production scheduling for continuous process is more difficult than that for discrete process or batch process. In continuous process, experience rules usually play very important roles. But it is impossible to obtain the optimal solution just according to the rules. A novel Generalized Disjunctive Programming model for multi-periodic continuous process scheduling is proposed. In this model, some logistic constraints and experience rules are represented in disjunctive forms, which make the model more completely. An example is used to illustrate the effectiveness of this method.

High-order cumulant-based adaptive filter using particle swarm optimization

High-order cumulant-based (HOC) adaptive filter can limit Gauss noise or other noise with symmetric probability distribution function. Current HOC-based adaptive filter commonly adopt gradient search method, but gradient search process is hard to avoid local convergence and complexity. Particle swarm optimization (PSO) is simple and easy to implement, and with no gradient information and other advantages, which can be used to solve many complex problems. Using PSO algorithm to optimize the filter coefficients was proposed as a new method, considering HOC-based coefficients adjustment of adaptive filter as an optimization problem. The simulation results show that using PSO can get higher precision in HOC-based coefficients optimization of adaptive filter. In addition, PSO algorithm is relatively affected little by system jump, which has certain advantage in non-stationary process model.

Robust Scheduling with Recourse for Batch Processes under Uncertainty

Uncertain phenomenon is prevalent in batch processes, which makes it very difficult to obtain a satisfied schedule for the decision maker with usual deterministic model. In order to improve the system performance in the face of uncertainty, a novel robust scheduling method based on the idea of robust optimization with recourse is proposed. The corn of this method is to find the equilibrium of feasibility and optimization by recourse. Some robust metrics are introduced and proposed to quantify the scheduling robustness. Then a novel robust scheduling model under demand uncertainty is proposed. In this model, some constraints could be violated, but must be compensated to realize robust optimization. Finally the simulation results illustrate the validity of this method

Production scheduling modeling of oil refinery considering crude oil switch transition

Planning and scheduling of the flow of crude oil are very important problems in petroleum refineries. Since they emphasize on different apartment of plant, there are more optimized space in scheduling layer after plan optimization. The core of this article is to treat the scheduling problem when transitions occur by feedstock from tanks to crude distillation units. A scheduling model is built as the aim of minimized operation cost in this paper. The model relies on continue time programming techniques, which more consider equipment statue and material property. A case study from a practical data show that re-optimization in scheduling layer is better than plan optimized result.

A hierarchy scheduling structure based on mathematical programming and controlled hybrid Petri nets

This paper presents a new kind of Petri net - controlled hybrid Petri nets. A hierarchy scheduling structure based on mathematical programming and controlled hybrid Petri nets is introduced. At the high level, mathematical programming is used to find the optimal instantaneous firing speed for the lower level and send control instruction to the lower level. A the lower level, controlled hybrid Petri nets are used to model the real manufacturing environment and accept the instruction from the upper level. Finally, the continuous process system of a chemical plant is taken as an example for illustration.