The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor...
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The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.
The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p-xylene (PX) to TPA is regarded as a critical and efficient chemi...
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The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p-xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [ 1 ]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta- neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob- lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). optimization results indicate that application oflSADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.
This brief deals with the problem of master-slave synchronization for chaotic Lur'e systems with aperiodic sampled data. Specifically, a novel aperiodic adaptive event-triggered communication mechanism is introduc...
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This brief deals with the problem of master-slave synchronization for chaotic Lur'e systems with aperiodic sampled data. Specifically, a novel aperiodic adaptive event-triggered communication mechanism is introduced to reduce the transmission load, which covers the previous ones as special cases. By partially resorting to the time-dependent Lyapunov function, a new synchronization criterion is derived, which depends on both the upper and lower bounds of variable sampling interval. Finally, Chua's circuit system is chosen as an illustrative example to show the virtue and effectiveness of the achieved synchronization strategies.
Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multip...
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Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables (MDNSGA-II) is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.
This paper is concerned with finite-time containment control problem for second-order nonlinear multi-agent systems with multiple dynamic leaders. Two new containment control protocols are developed to ensure that all...
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Independent component analysis( ICA) has been widely applied to the monitoring of non-Gaussian processes. Despite lots of applications,there is no universally accepted criterion to select the dominant independent comp...
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Independent component analysis( ICA) has been widely applied to the monitoring of non-Gaussian processes. Despite lots of applications,there is no universally accepted criterion to select the dominant independent components( ICs). Moreover, how to determine the number of dominant ICs is still an open question. To further address this issue,a novel process monitoring based on IC contribution( ICC) is proposed from the perspective of information storage. Based on the ICC with each variable,the dominant ICs can be obtained and the number of dominant ICs is determined objectively. To further preserve the process information, the remaining ICs are not useless. As a result,all the ICs are regarded to be divided into dominant and residual subspaces. The monitoring models are established respectively in each subspace, and then Bayesian inference is applied to integrating monitoring results of the two subspaces. Finally, the feasibility and effectiveness of the proposed method are illustrated through a numerical example and the Tennessee Eastman process.
Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the...
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Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the *** solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.
This paper considers the problem of semi-global leader-following consensus of a multi-agent system whose agent dynamics are represented by linear systems. The input output characteristics of the follower agent actuato...
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This paper considers the problem of semi-global leader-following consensus of a multi-agent system whose agent dynamics are represented by linear systems. The input output characteristics of the follower agent actuators, such as those of saturation and dead-zone, are imperfect, not precisely known, and subject to the effect of disturbances. Two consensus control algorithms, of the low-and-high gain feedback type and the low gain based variable structure control type, are proposed for solving the consensus problem. It is shown that both of these control algorithms achieve semi-global leader-following practical consensus in the presence of the imperfectness of the actuators when the communication topology among the follower agents is represented by a strongly connected and detailed balanced directed graph and the leader agent is a neighbor of at least one follower agent. The theoretical results are illustrated by numerical simulation.
The model reduction problem is studied in this work for the switched genetic regulatory networks(GRNs) with timevarying delays. The attention is focused on constructing a reduced-order model to approximate the consi...
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ISBN:
(纸本)9781538629185
The model reduction problem is studied in this work for the switched genetic regulatory networks(GRNs) with timevarying delays. The attention is focused on constructing a reduced-order model to approximate the considered high-order GRNs under that the switching signal is subject to some certain constraints, such that the error system between the original system and the reduced-order one is exponentially stable with a weighted H∞ performance. By utilizing the bounding technique as well as the dwell time method, the stability conditions and the weighted H performance are established for the error system. Then, the solvability conditions for the reduced-order models for the GRNs are also established by using the projection method. Finally,numerical simulation is presented to illustrate the effectiveness of the proposed method.
In this paper,we consider the problem of predictive control for a class of coupled linear *** solving a set of local optimization problems with decoupled cost functions and constraints,an event-triggered decentralized...
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ISBN:
(纸本)9781538629185
In this paper,we consider the problem of predictive control for a class of coupled linear *** solving a set of local optimization problems with decoupled cost functions and constraints,an event-triggered decentralized predictive control(DPC) scheme is *** event-triggering conditions only involving local information of every subsystem is derived and sufficient conditions of the recursive feasibility and the stability of close-loop control systems are also ***,a numerical example is given.
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