Traditional level measurements based on sensors were easily affected by circumstance especially in severe industrial process,whose accuracy of measurement cannot meet the control *** order to deal with the problem,a n...
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ISBN:
(纸本)9781509046584
Traditional level measurements based on sensors were easily affected by circumstance especially in severe industrial process,whose accuracy of measurement cannot meet the control *** order to deal with the problem,a new level measurement method based on randomized Hough Transform was *** pre-processing,noise-reduction and filtering to the original image,the randomized Hough Transform was used to recognize the liquid *** results showed that the error of the proposed method is within 2%and the average recognition time is within 0.1 *** proposed method can meet the requirement in industrial fields.
Considering the imprecise nature of the data in real-world problems, the earliness/tardiness (E/T) fiowshop scheduling problem with uncertain processing time and distinct due windows is concerned in this paper. A fu...
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Considering the imprecise nature of the data in real-world problems, the earliness/tardiness (E/T) fiowshop scheduling problem with uncertain processing time and distinct due windows is concerned in this paper. A fuzzy scheduling model is established and then transformed into a deterministic one by employing the method of maximizing the membership function of middle value. Moreover, an effective scatter search based particle swarm optimization (SSPSO) algorithm is proposed to minimize the sum of total earliness and tardiness penalties. The proposed SSPSO algorithm incorporates the scatter search (SS) algorithm into the frame of particle swarm optimization (PSO) algorithm and gives full play to their characteristics of fast convergence and high diversity. Besides, a differential evolution (DE) scheme is used to generate solutions in the SS. In addition, the dynamic update strategy and critical conditions are adopted to improve the performance of SSPSO. The simulation results indicate the superiority of SSPSO in terms of effectiveness and efficiency.
Many systems composed by several interacting subsystems are usually controlled by a distributed control framework. Distributed Model Predictive control(DMPC) strategy, in which each subsystem is controlled by a local ...
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ISBN:
(纸本)9781479947249
Many systems composed by several interacting subsystems are usually controlled by a distributed control framework. Distributed Model Predictive control(DMPC) strategy, in which each subsystem is controlled by a local MPC controller, has advantages of accommodating constraints, less computational cost and high flexibility. In order to improve the global performance and guarantee the system stability, a stabilized DMPC strategy is proposed in this paper, in which subsystems interact through inputs. At first, local initial feasible solutions are achieved based on a Minkowski functional to guarantee the local closed-loop system stabilization. And then the global optimal solutions are obtained through coordination strategy for the sake of reducing iteration time and accelerating the convergence speed efficiently. Finally, the accuracy and efficiency of the proposed scheme is put to test through simulation.
Quality-related fault detection has received extensive attention in recent years. It requires an appropriate supervisory relationship between process variables and quality variables. While the traditional principal co...
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ISBN:
(纸本)9781509046584
Quality-related fault detection has received extensive attention in recent years. It requires an appropriate supervisory relationship between process variables and quality variables. While the traditional principal component analysis(PCA) doesn't consider the relationships between them. Thus we proposed the mutual information principal component analysis(MIPCA) to detect the quality-related faults. MIPCA fully integrates the advantages of mutual information(MI) and PCA. With MIPCA, process variables can be utilized to monitor the process under the supervision of quality variables and judge a fault is whether related to the quality or not. Finally, the feasibility and effectiveness of the MIPCA are verified in Tennessee Eastman Process(TEP).
In this paper,a neural network based model predictive control(MPC) strategy is proposed to for the cellular uptake(as a function of space and time) of a ***,a time/space separation method is used to transform the ...
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ISBN:
(纸本)9781538629185
In this paper,a neural network based model predictive control(MPC) strategy is proposed to for the cellular uptake(as a function of space and time) of a ***,a time/space separation method is used to transform the high-dimensional spatiotemporal data into low-dimensional temporal ***,the MPC strategy is posed using the identified temporal radial basis function neural network *** the solution of NN-based MPC is obtained by a golden section method that can shorten the solution time of the optimization *** accuracy and effectiveness of this approach are demonstrated on an example motivated by tissue engineering.
In this paper,we consider the privacy preserving problem of consensus ***,we introduce a privacy preserving scheme,where each node produces and transmits a sequence of random values with their mean equaling to the nod...
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ISBN:
(纸本)9781538629185
In this paper,we consider the privacy preserving problem of consensus ***,we introduce a privacy preserving scheme,where each node produces and transmits a sequence of random values with their mean equaling to the node's initial *** show that the network can reach average consensus with privacy preserving scheme,and provide a sufficient condition under which the initial state of one node can be inferred ***,we study the privacy preserving performance when there exists an attacker who can intercept the data transmitted on the *** a ring network,aiming at the attacker with limited power,we find the optimal attacking strategy to maximize the probability of the privacy ***,the simulations verify the derived theoretical results.
Given the significant requirements for transforming and promoting the process industry, we present themajor limitations of current petrochemical enterprises, including limitations in decision-making, produc-tion opera...
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Given the significant requirements for transforming and promoting the process industry, we present themajor limitations of current petrochemical enterprises, including limitations in decision-making, produc-tion operation, efficiency and security, information integration, and so forth. To promote a vision of theprocess industry with efficient, green, and smart production, modern information technology should beutilized throughout the entire optimization process for production, management, and marketing. To focuson smart equipment in manufacturing processes, as well as on the adaptive intelligent optimization of themanufacturing process, operating mode, and supply chain management, we put forward several key scien-tific problems in engineering in a demand-driven and application-oriented manner, namely:intelligentsensing and integration of all process information, including production and management information; collaborative decision-making in the supply chain, industry chain, and value chain, driven by knowledge; cooperative control and optimization of plant-wide production processes via human-cyber-physical in-teraction; and Q life-cycle assessments for safety and environmental footprint monitoring, in addition totracing analysis and risk control. In order to solve these limitations and core scientific problems, we furtherpresent fundamental theories and key technologies for smart and optimal manufacturing in the processindustry. Although this paper discusses the process industry in China, the conclusions in this paper can beextended to the larocess industry around the world.
In this paper, we propose a novel performance monitoring and fault detection method, which is based on modified structure analysis and globality and locality preserving (MSAGL) projection, for non-Gaussian processes...
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In this paper, we propose a novel performance monitoring and fault detection method, which is based on modified structure analysis and globality and locality preserving (MSAGL) projection, for non-Gaussian processes with multiple operation conditions. By using locality preserving projection to analyze the embedding geometrical manifold and extracting the non-Gaussian features by independent component analysis, MSAGL preserves both the global and local structures of the data simultaneously. Furthermore, the tradeoff parameter of MSAGL is tuned adaptively in order to find the projection direction optimal for revealing the hidden structural information. The validity and effectiveness of this approach are illustrated by applying the proposed technique to the Tennessee Eastman process simulation under multiple operation conditions. The results demonstrate the advantages of the proposed method over conventional eigendecomposition-based monitoring methotis.
Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-di...
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Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.
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.
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