Considering that outliers can disrupt the correlation structure of least square support vector machine (LS-SVM), and that the parameters of LS-SVM play an important role in the performance, a novel weighted least squa...
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Cracking gas compressor is usually a centrifugal compressor. The information on the performance of a centrifugal compressor under all conditions is not available, which restricts the operation optimization for compres...
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Cracking gas compressor is usually a centrifugal compressor. The information on the performance of a centrifugal compressor under all conditions is not available, which restricts the operation optimization for compressor. To solve this problem, two back propagation (BP) neural networks were introduced to model the performance of a compressor by using the data provided by manufacturer. The input data of the model under other conditions should be corrected according to the similarity theory. The method was used to optimize the system of a cracking gas compressor by embedding the compressor performance model into the ASPEN PLUS model of compressor. The result shows that it is an effective method to optimize the compressor system.
This paper develops an improved particle swarm optimization algorithm based on cultural algorithm for constrained optimization problems. Firstly, chaos method is utilized in the initialization process of single swarm ...
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In the paper, a new process monitoring approach is proposed for handling the multimode problem in the industrial processes. The original space can be separated into two different parts, which are the common part and t...
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The ammonia synthesis section is the core during the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects the production effic...
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The ammonia synthesis section is the core during the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects the production efficiency directly. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied to compare with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.
For partial differential equation description unknown spatially distributed systems, the number of local models determines the dimension of the model. So far, there is no mature method about how to obtain the optimal ...
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Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM...
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Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM) methods are based on the assumption that the process has only one nominal mode. When the process data contain different distributions, they may not function as well as in single mode processes. To address this issue, an improved partial least squares (IPLS) method was proposed for multimode process monitoring. By utilizing a novel local standardization strategy, the normal data in multiple modes could be centralized after being standardized and the fundamental assumption of partial least squares (PLS) could be valid again in multimode process. In this way, PLS method was extended to be suitable for not only single mode processes but also multimode processes. The efficiency of the proposed method was illustrated by comparing the monitoring results of PLS and IPLS in Tennessee Eastman(TE) process.
In the applications of wireless sensor networks(WSNs), sensor energy saving is essential to increase the life of sensor networks. In this paper, we consider the problem of performing consensus based estimation over en...
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In the applications of wireless sensor networks(WSNs), sensor energy saving is essential to increase the life of sensor networks. In this paper, we consider the problem of performing consensus based estimation over energy constrained WSNs, in which energy is conserved by selecting only a subset of sensors to observe the state of the dynamical system at each time step. First, we derive an sufficient condition for the convergence of the state estimation covariance. Second, we propose a sensor selection strategy to schedule sensors to measure the system state for next step with the goal of minimizing the state estimation error subject to sensor energy constraint. Finally, we provide some numerical examples to illustrate the performance and effectiveness of the proposal strategy.
With a multitude of reaction pathways, poly (ethylene-terephthalate) (PET) polymerization of industrial practice is complex, and the quality of PET is normally described in terms of several experimentally measured ind...
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It is known that the characteristic of Dissolved Oxygen (DO) control system is non-linear, variability and uncertainty, etc. This paper presents Genetic Algorithm (GA) to optimize the fuzzy controller, which adjusts t...
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It is known that the characteristic of Dissolved Oxygen (DO) control system is non-linear, variability and uncertainty, etc. This paper presents Genetic Algorithm (GA) to optimize the fuzzy controller, which adjusts the membership function of fuzzy controller parameters by GA. The results show that the optimized fuzzy controller based on GA is well up for the shortage of the traditional fuzzy controller. With this controller we can get target such as: obtaining better control result, improving the efficiency of sewage treatment, having significant effect for stable and secure running and reducing energy consumption.
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