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14 pages, 291 KiB

Open AccessFeature PaperArticle

Extremum Seeking Control for Discrete-Time with Quantized and Saturated Actuators

by Martin Guay and Daniel J. Burns

Processes 2019, 7(11), 831; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7110831 - 08 Nov 2019

Cited by 4 | Viewed by 2019

This paper proposes an extremum-seeking controller (ESC) design for a class of discrete-time nonlinear control systems subject to input constraints or quantized inputs. The proposed method implements a proportional-integral ESC design along with a discrete-time anti-windup mechanism. The anti-windup enforces input saturation while [...] Read more.

This paper proposes an extremum-seeking controller (ESC) design for a class of discrete-time nonlinear control systems subject to input constraints or quantized inputs. The proposed method implements a proportional-integral ESC design along with a discrete-time anti-windup mechanism. The anti-windup enforces input saturation while preserving the input dither signal. The technique incorporates a mechanism for adjusting the amplitude of the extremum seeking control dither signal. This mechanism ensures that any violation of constraints due to the dither signal is removed while maintaining the probing signal active. An amplitude update routine is also proposed. The amplitude update is coupled with a saturation bias estimation algorithm that correctly accounts for the inherent bias associated with systems operated at or near saturation conditions. The amplitude update is designed to remove the dither signal when the system approaches the optimum. It also ensures that a lower bound of the amplitude is enforced to guarantee that excitation conditions are maintained. Full article

(This article belongs to the Special Issue Process Systems Engineering à la Canada)

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19 pages, 553 KiB

Open AccessFeature PaperArticle

An Optimal Feedback Control Strategy for Nonlinear, Distributed-Parameter Processes

by Debaprasad Dutta and Simant Ranjan Upreti

Processes 2019, 7(10), 758; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7100758 - 17 Oct 2019

Cited by 3 | Viewed by 2984

Abstract

In this work, an optimal state feedback control strategy is proposed for non-linear, distributed-parameter processes. For different values of a given parameter susceptible to upsets, the strategy involves off-line computation of a repository of optimal open-loop states and gains needed for the feedback [...] Read more.

In this work, an optimal state feedback control strategy is proposed for non-linear, distributed-parameter processes. For different values of a given parameter susceptible to upsets, the strategy involves off-line computation of a repository of optimal open-loop states and gains needed for the feedback adjustment of control. A gain is determined by minimizing the perturbation of the objective functional about the new optimal state and control corresponding to a process upset. When an upset is encountered in a running process, the repository is utilized to obtain the control adjustment required to steer the process to the new optimal state. The strategy is successfully applied to a highly non-linear, gas-based heavy oil recovery process controlled by the gas temperature with the state depending non-linearly on time and two spatial directions inside a moving boundary, and subject to pressure upsets. The results demonstrate that when the process has a pressure upset, the proposed strategy is able to determine control adjustments with negligible time delays and to navigate the process to the new optimal state. Full article

(This article belongs to the Special Issue Process Systems Engineering à la Canada)

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17 pages, 1892 KiB

Open AccessArticle

An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes

by Mengfei Zhou, Qiang Zhang, Yunwen Liu, Xiaofang Sun, Yijun Cai and Haitian Pan

Processes 2019, 7(10), 648; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7100648 - 22 Sep 2019

Cited by 24 | Viewed by 2930

Abstract

Pipelines are one of the most efficient and economical methods of transporting fluids, such as oil, natural gas, and water. However, pipelines are often subject to leakage due to pipe corrosion, pipe aging, pipe weld defects, or damage by a third-party, resulting in [...] Read more.

Pipelines are one of the most efficient and economical methods of transporting fluids, such as oil, natural gas, and water. However, pipelines are often subject to leakage due to pipe corrosion, pipe aging, pipe weld defects, or damage by a third-party, resulting in huge economic losses and environmental degradation. Therefore, effective pipeline leak detection methods are important research issues to ensure pipeline integrity management and accident prevention. The conventional methods for pipeline leak detection generally need to extract the features of leak signal to establish a leak detection model. However, it is difficult to obtain actual leakage signal data samples in most applications. In addition, the operating modes of pipeline fluid transportation process often have frequent changes, such as regulating valves and pump operation. Aiming at these issues, this paper proposes a hybrid intelligent method that integrates kernel principal component analysis (KPCA) and cascade support vector data description (Cas-SVDD) for pipeline leak detection with multiple operating modes, using data samples that are leak-free during pipeline operation. Firstly, the local mean decomposition method is used to denoise and reconstruct the measured signal to obtain the feature variables. Then, the feature dimension is reduced and the nonlinear principal component is extracted by the KPCA algorithm. Secondly, the K-means clustering algorithm is used to identify multiple operating modes and then obtain multiple support vector data description models to obtain the decision boundaries of the corresponding hyperspheres. Finally, pipeline leak is detected based on the Cas-SVDD method. The experimental results show that the proposed method can effectively detect small leaks and improve leak detection accuracy. Full article

(This article belongs to the Special Issue Process Systems Engineering à la Canada)

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14 pages, 4718 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

BISSO: Biomass Interface for Superstructure Simulation and Optimization

by Franco Mangone, Jimena Ferreira and Ana I. Torres

Processes 2019, 7(10), 645; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7100645 - 21 Sep 2019

Viewed by 2583

Abstract

This paper presents a web-based tool for the optimization of biomass-to-chemicals processing pathways. The tool provides a user-friendly grpahical user interface (GUI) for building a process superstructure, offers the possibility of uploading data from Aspen Plus simulations and generates an optimization code to [...] Read more.

This paper presents a web-based tool for the optimization of biomass-to-chemicals processing pathways. The tool provides a user-friendly grpahical user interface (GUI) for building a process superstructure, offers the possibility of uploading data from Aspen Plus simulations and generates an optimization code to find the pathway that minimizes the annualized costs or maximizes the net present value. A processing pathway from residues to lactic acid is used to discuss and illustrate the main features of the tool. Full article

(This article belongs to the Special Issue Process Systems Engineering à la Canada)

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42 pages, 4831 KiB

Open AccessFeature PaperArticle

A Comparison of Clustering and Prediction Methods for Identifying Key Chemical–Biological Features Affecting Bioreactor Performance

by Yiting Tsai, Susan A. Baldwin, Lim C. Siang and Bhushan Gopaluni

Processes 2019, 7(9), 614; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7090614 - 10 Sep 2019

Cited by 2 | Viewed by 3551

Abstract

Chemical–biological systems, such as bioreactors, contain stochastic and non-linear interactions which are difficult to characterize. The highly complex interactions between microbial species and communities may not be sufficiently captured using first-principles, stationary, or low-dimensional models. This paper compares and contrasts multiple data analysis [...] Read more.

Chemical–biological systems, such as bioreactors, contain stochastic and non-linear interactions which are difficult to characterize. The highly complex interactions between microbial species and communities may not be sufficiently captured using first-principles, stationary, or low-dimensional models. This paper compares and contrasts multiple data analysis strategies, which include three predictive models (random forests, support vector machines, and neural networks), three clustering models (hierarchical, Gaussian mixtures, and Dirichlet mixtures), and two feature selection approaches (mean decrease in accuracy and its conditional variant). These methods not only predict the bioreactor outcome with sufficient accuracy, but the important features correlated with said outcome are also identified. The novelty of this work lies in the extensive exploration and critique of a wide arsenal of methods instead of single methods, as observed in many papers of similar nature. The results show that random forest models predict the test set outcomes with the highest accuracy. The identified contributory features include process features which agree with domain knowledge, as well as several different biomarker operational taxonomic units (OTUs). The results reinforce the notion that both chemical and biological features significantly affect bioreactor performance. However, they also indicate that the quality of the biological features can be improved by considering non-clustering methods, which may better represent the true behaviour within the OTU communities. Full article

(This article belongs to the Special Issue Process Systems Engineering à la Canada)

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24 pages, 3267 KiB

Open AccessFeature PaperArticle

Discrete-Time Kalman Filter Design for Linear Infinite-Dimensional Systems

by Junyao Xie and Stevan Dubljevic

Processes 2019, 7(7), 451; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7070451 - 15 Jul 2019

Cited by 6 | Viewed by 3273

Abstract

As the optimal linear filter and estimator, the Kalman filter has been extensively utilized for state estimation and prediction in the realm of lumped parameter systems. However, the dynamics of complex industrial systems often vary in both spatial and temporal domains, which take [...] Read more.

As the optimal linear filter and estimator, the Kalman filter has been extensively utilized for state estimation and prediction in the realm of lumped parameter systems. However, the dynamics of complex industrial systems often vary in both spatial and temporal domains, which take the forms of partial differential equations (PDEs) and/or delay equations. State estimation for these systems is quite challenging due to the mathematical complexity. This work addresses discrete-time Kalman filter design and realization for linear distributed parameter systems. In particular, the structural- and energy-preserving Crank–Nicolson framework is applied for model time discretization without spatial approximation or model order reduction. In order to ensure the time instance consistency in Kalman filter design, a new discrete model configuration is derived. To verify the feasibility of the proposed design, two widely-used PDEs models are considered, i.e., a pipeline hydraulic model and a 1D boundary damped wave equation. Full article

(This article belongs to the Special Issue Process Systems Engineering à la Canada)

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18 pages, 1343 KiB

Open AccessArticle

Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization

by Weiqin Ying, Hassan Jalil, Bingshen Wu, Yu Wu, Zhenyu Ying, Yucheng Luo and ZhenYu Wang

Processes 2019, 7(2), 111; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7020111 - 20 Feb 2019

Cited by 5 | Viewed by 3198

Abstract

Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP [...] Read more.

Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP effectively and efficiently. In consideration of the global properties of the selection and update mechanisms, PCACD employs a global island model and targeted elitist migration policy in a conical area evolutionary algorithm (CAEA) to discover community structures at different resolutions in parallel. Although each island is assigned only a portion of all sub-problems in the island model, it preserves a complete population to accomplish the global selection and update. Meanwhile the migration policy directly migrates each elitist individual to an appropriate island in charge of the sub-problem associated with this individual to share essential evolutionary achievements. In addition, a modularity-based greedy local search strategy is also applied to accelerate the convergence rate. Comparative experimental results on six real-world networks reveal that PCACD is capable of discovering potential high-quality community structures at diverse resolutions with satisfactory running efficiencies. Full article

(This article belongs to the Special Issue Process Systems Engineering à la Canada)

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7 pages, 174 KiB

Open AccessFeature PaperCommentary

Data Science in the Chemical Engineering Curriculum

by Thomas A. Duever

Processes 2019, 7(11), 830; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7110830 - 08 Nov 2019

Cited by 8 | Viewed by 4114

Abstract

With the increasing availability of large amounts of data, methods that fall under the term data science are becoming important assets for chemical engineers to use. Methods, broadly speaking, are needed to carry out three tasks, namely data management, statistical and machine learning [...] Read more.

With the increasing availability of large amounts of data, methods that fall under the term data science are becoming important assets for chemical engineers to use. Methods, broadly speaking, are needed to carry out three tasks, namely data management, statistical and machine learning and data visualization. While claims have been made that data science is essentially statistics, consideration of the three tasks previously mentioned make it clear that it is really broader than just statistics alone and furthermore, statistical methods from a data-poor era are likely insufficient. While there have been many successful applications of data science methodologies, there are still many challenges that must be addressed. For example, just because a dataset is large, does not necessarily mean it is meaningful or information rich. From an organizational point of view, a lack of domain knowledge and a lack of a trained workforce among other issues are cited as barriers for the successful implementation of data science within an organization. Many of the methodologies employed in data science are familiar to chemical engineers; however, it is generally the case that not all the methods required to carry out data science projects are covered in an undergraduate chemical engineering program. One option to address this is to adjust the curriculum by modifying existing courses and introducing electives. Other examples include the introduction of a data science minor or a postgraduate certificate or a Master’s program in data science. Full article

(This article belongs to the Special Issue Process Systems Engineering à la Canada)

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