An efficient latent variable optimization approach with stochastic constraints for complex industrial process

Purpose This paper aims to investigate an identification strategy for the nonlinear state-space model (SSM) in the presence of an unknown output time-delay. The equations to estimate the unknown model parameters and output time-delay are derived simultaneously in the proposed strategy. Design/methodology/approach The unknown integer-valued time-delay is processed as a latent variable which is uniformly distributed in a priori known range. The estimations of the unknown time-delay and model parameters are both realized using the Expectation-Maximization (EM) algorithm, which has a good performance in dealing with latent variable issues. Moreover, the particle filter (PF) with an unknown time-delay is introduced to calculated the Q-function of the EM algorithm. Findings Although amounts of effective approaches for nonlinear SSM identification have been developed in the literature, the problem of time-delay is not considered in most of them. The time-delay is commonly existed in industrial scenario and it could cause extra difficulties for industrial process modeling. The problem of unknown output time-delay is considered in this paper, and the validity of the proposed approach is demonstrated through the numerical example and a two-link manipulator system. Originality/value The novel approach to identify the nonlinear SSM in the presence of an unknown output time-delay with EM algorithm is put forward in this work.

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Design and Implementation of Energy Efficiency in HVAC Systems Based on Robust PID Control for Industrial Applications

Journal of Sensors ◽

10.1155/2015/954159 ◽

2015 ◽

Vol 2015 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Muharrem Imal

Keyword(s):

Energy Efficiency ◽

Optimization Problems ◽

Industrial Process ◽

Weather Conditions ◽

Hvac Systems ◽

Industrial Applications ◽

Optimization Approach ◽

Primary Concern ◽

Dynamic Cooling ◽

Alternative Current

Energy efficiency in heating, ventilating, and air-conditioning (HVAC) systems is a primary concern in process projects, since the energy consumption has the highest percentage in HVAC for all processes. Without sacrifice of thermal comfort, to reset the suitable operating parameters, such as the humidity and air temperature, would have energy saving with immediate effect. In this paper, the simulation-optimization approach described the effective energy efficiency for HVAC systems which are used in industrial process. Due to the complex relationship of the HVAC system parameters, it is necessary to suggest optimum settings for different operations in response to the dynamic cooling loads and changing weather conditions during a year. Proportional-integral-derivative (PID) programming was developed which can effectively handle the discrete, nonlinear and highly constrained optimization problems. Energy efficiency process has been made by controlling of alternative current (AC) drivers for ventilation and exhaust fans, according to supplied air flow capacity and differential air pressure between supplied and exhaust air. Supervisory controller software was developed by using programmable controllers and human machine interface (HMI) units. The new designed HVAC control system would have a saving potential of about 40% as compared to the existing operational settings, without any extra cost.

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Selective Hydrogenation of Methylacetylene and Propadiene in an Industrial Process: A Multiobjective Optimization Approach

Industrial & Engineering Chemistry Research ◽

10.1021/ie100994j ◽

2011 ◽

Vol 50 (3) ◽

pp. 1453-1459 ◽

Cited By ~ 5

Author(s):

Wei Wu ◽

Yu-Lu Li

Keyword(s):

Multiobjective Optimization ◽

Selective Hydrogenation ◽

Industrial Process ◽

Optimization Approach

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Retrospective comparison of several typical linear dynamic latent variable models for industrial process monitoring

Computers & Chemical Engineering ◽

10.1016/j.compchemeng.2021.107587 ◽

2021 ◽

pp. 107587

Author(s):

Jiale Zheng ◽

Chunhui Zhao ◽

Furong Gao

Keyword(s):

Process Monitoring ◽

Latent Variable ◽

Industrial Process ◽

Latent Variable Models ◽

Linear Dynamic ◽

Retrospective Comparison

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Differential Effects of Executive Processes on Working Memory

Journal of Individual Differences ◽

10.1027/1614-0001/a000211 ◽

2016 ◽

Vol 37 (4) ◽

pp. 239-249

Author(s):

Xuezhu Ren ◽

Tengfei Wang ◽

Karl Schweizer ◽

Jing Guo

Keyword(s):

Working Memory ◽

Cognitive Processes ◽

Latent Variable ◽

Working Memory Capacity ◽

Memory Capacity ◽

Attention Control ◽

Executive Processes ◽

Differential Effects ◽

Prepotent Response ◽

Two Measures

Abstract. Although attention control accounts for a unique portion of the variance in working memory capacity (WMC), the way in which attention control contributes to WMC has not been thoroughly specified. The current work focused on fractionating attention control into distinctly different executive processes and examined to what extent key processes of attention control including updating, shifting, and prepotent response inhibition were related to WMC and whether these relations were different. A number of 216 university students completed experimental tasks of attention control and two measures of WMC. Latent variable analyses were employed for separating and modeling each process and their effects on WMC. The results showed that both the accuracy of updating and shifting were substantially related to WMC while the link from the accuracy of inhibition to WMC was insignificant; on the other hand, only the speed of shifting had a moderate effect on WMC while neither the speed of updating nor the speed of inhibition showed significant effect on WMC. The results suggest that these key processes of attention control exhibit differential effects on individual differences in WMC. The approach that combined experimental manipulations and statistical modeling constitutes a promising way of investigating cognitive processes.

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Probability-Based and Measurement-Related Hypotheses With Full Restriction for Investigations by Means of Confirmatory Factor Analysis

Methodology ◽

10.1027/1614-2241/a000033 ◽

2011 ◽

Vol 7 (4) ◽

pp. 157-164

Author(s):

Karl Schweizer

Keyword(s):

Factor Analysis ◽

Confirmatory Factor Analysis ◽

Cognitive Processing ◽

Latent Variables ◽

Repeated Measures ◽

Latent Variable ◽

Model Fit ◽

Repeated Measures Data ◽

Confirmatory Factor ◽

And Performance

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

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