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Processes
Special Issues
Process Control: Current Trends and Future Challenges
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Process Control: Current Trends and Future Challenges

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (31 December 2014) | Viewed by 44757

Special Issue Editor

Prof. Dr. Gabriele Pannocchia

E-Mail Website
Guest Editor
Department of Civil and Industrial Engineering-Chemical Engineering Section, University of Pisa, Largo L. Lazzarino, 2, 56126 Pisa, Italy
Interests: model predictive control; process modeling, simulation and optimization; efficient numerical algorithms; biomedical systems modeling and advanced control algorithms; multivariable system identification and performance monitoring; optimal robotic manipulation and locomotion.

Special Issue Information

Dear Colleagues,

The area of process control has changed significantly over the last few decades, in terms of methods, algorithms, and application domains. Cost and energy reduction needs have prompted process and control engineers to develop and adopt optimization-based control systems, which nowadays pervade many process industries. Advanced process control systems have optimization objectives to meet, not just regulation tasks to perform; to meet these objectives, a strong and delicate blend of advanced hardware (sensors and actuators), software (algorithms), and process knowledge (modeling) is required.

The Special Issue, "Process Control: Current Trends and Future Challenges" of the journal Processes, seeks contributions to assess the state-of-the-art and future challenges in the wide area of process control; topics include, but are not limited to: optimization-based control and estimation methods, distributed control architectures for plant-wide optimization, process monitoring, diagnosis and fault detection, process dynamic modeling and identification for advanced control systems, applications of advanced instrumentation, and soft sensors.

Papers involving industrial collaborations are particularly encouraged in order to provide the reader with a clear assessment of the current best practice, and to better understand which challenges are likely to be faced in the near future.

Dr. Gabriele Pannocchia
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optimization based control (MPC) and estimation methods (MHE)
  • distributed optimization-based control
  • process monitoring, diagnosis and fault detection
  • process dynamic modeling
  • process identification
  • advanced instrumentation
  • soft sensors

Published Papers (7 papers)

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Research

Jump to: Review

1457 KiB  
Article
Computer-Aided Framework for the Design of Freeze-Drying Cycles: Optimization of the Operating Conditions of the Primary Drying Stage
by Davide Fissore and Roberto Pisano
Processes 2015, 3(2), 406-421; https://doi.org/10.3390/pr3020406 - 25 May 2015
Cited by 39 | Viewed by 6417
Abstract
This paper deals with the freeze-drying process and, in particular, with the optimization of the operating conditions of the primary drying stage. When designing a freeze-drying cycle, process control aims at obtaining the values of the operating conditions (temperature of the heating fluid [...] Read more.
This paper deals with the freeze-drying process and, in particular, with the optimization of the operating conditions of the primary drying stage. When designing a freeze-drying cycle, process control aims at obtaining the values of the operating conditions (temperature of the heating fluid and pressure in the drying chamber) resulting in a product temperature lower than the limit value of the product, and in the shortest drying time. This is particularly challenging, mainly due to the intrinsic nonlinearity of the system. In this framework, deep process knowledge is required for deriving a suitable process dynamic model that can be used to calculate the design space for the primary drying stage. The design space can then be used to properly design (and optimize) the process, preserving product quality. The case of a product whose dried layer resistance, one of the key model parameters, is affected by the operating conditions is addressed in this paper, and a simple and effective method to calculate the design space in this case is presented and discussed. Full article
(This article belongs to the Special Issue Process Control: Current Trends and Future Challenges)
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396 KiB  
Article
An Algorithm for Finding Process Identification Intervals from Normal Operating Data
by André C. Bittencourt, Alf J. Isaksson, Daniel Peretzki and Krister Forsman
Processes 2015, 3(2), 357-383; https://doi.org/10.3390/pr3020357 - 06 May 2015
Cited by 29 | Viewed by 5418
Abstract
Performing experiments for system identification is often a time-consuming task which may also interfere with the process operation. With memory prices going down and the possibility of cloud storage, years of data is more and more commonly stored (without compression) in a history [...] Read more.
Performing experiments for system identification is often a time-consuming task which may also interfere with the process operation. With memory prices going down and the possibility of cloud storage, years of data is more and more commonly stored (without compression) in a history database. In such stored data, there may already be intervals informative enough for system identification. Therefore, the goal of this project was to find an algorithm that searches and marks intervals suitable for process identification (rather than completely autonomous system identification). For each loop, four stored variables are required: setpoint, manipulated variable, measured process output and mode of the controller. The essential features of the method are the search for excitation of the input and output, followed by the estimation of a Laguerre model combined with a hypothesis test to check that there is a causal relationship between process input and output. The use of Laguerre models is crucial to handle processes with deadtime without explicit delay estimation. The method was tested on three years of data from about 200 control loops. It was able to find all intervals in which known identification experiments were performed as well as many other useful intervals in closed/open loop operation. Full article
(This article belongs to the Special Issue Process Control: Current Trends and Future Challenges)
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814 KiB  
Article
A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets
by Franklin D. Rincón, Galo A. C. Le Roux and Fernando V. Lima
Processes 2015, 3(2), 257-285; https://doi.org/10.3390/pr3020257 - 22 Apr 2015
Cited by 7 | Viewed by 5292
Abstract
This paper introduces a novel steady-state identification (SSI) method based on the auto-regressive model with exogenous inputs (ARX). This method allows the SSI with reduced tuning by analyzing the identifiability properties of the system. In particular, the singularity of the model matrices is [...] Read more.
This paper introduces a novel steady-state identification (SSI) method based on the auto-regressive model with exogenous inputs (ARX). This method allows the SSI with reduced tuning by analyzing the identifiability properties of the system. In particular, the singularity of the model matrices is used as an index for steady-state determination. In this contribution, the novel SSI method is compared to other available techniques, namely the F-like test, wavelet transform and a polynomial-based approach. These methods are implemented for SSI of three different case studies. In the first case, a simulated dataset is used for calibrating the output-based SSI methods. The second case corresponds to a literature nonlinear continuous stirred-tank reactor (CSTR) example running at different steady states in which the ARX-based approach is tuned with the available input-output data. Finally, an industrial case with real data of a depropanizer column from PETROBRAS S.A. considering different pieces of equipment is analyzed. The results for a reflux drum case indicate that the wavelet and the F-like test can satisfactorily detect the steady-state periods after careful tuning and when respecting their hypothesis, i.e., smooth data for the wavelet method and the presence of variance in the data for the F-like test. Through a heat exchanger case with different measurement frequencies, we demonstrate the advantages of using the ARX-based method over the other techniques, which include the aspect of online implementation. Full article
(This article belongs to the Special Issue Process Control: Current Trends and Future Challenges)
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415 KiB  
Article
Fast Wavelet-Based Model Predictive Control of Differentially Flat Systems
by Ruigang Wang, Michael James Tippett and Jie Bao
Processes 2015, 3(1), 161-177; https://doi.org/10.3390/pr3010161 - 11 Mar 2015
Cited by 5 | Viewed by 5847
Abstract
A system is differentially flat if it is Lie–Bäcklund (L-B) equivalent to a free dynamical system that has dimensions equal to that of the input of the original system. Utilizing this equivalence, the problem of nonlinear model predictive control of a flat system [...] Read more.
A system is differentially flat if it is Lie–Bäcklund (L-B) equivalent to a free dynamical system that has dimensions equal to that of the input of the original system. Utilizing this equivalence, the problem of nonlinear model predictive control of a flat system can be reduced to a lower dimensional nonlinear programming problem with respect to the flat outputs. In this work, a novel computational method based on Haar wavelets in the time-domain for solving the resulting nonlinear programming problem is developed to obtain an approximation of the optimal flat output trajectory. The Haar wavelet integral operational matrix is utilized to transform the nonlinear programming problem to a finite dimensional nonlinear optimization problem. The proposed approach makes use of flatness as a structural property of nonlinear systems and the convenient mathematical properties of Haar wavelets to develop an efficient computational algorithm for nonlinear model predictive control of differentially flat systems. Further improvement on computational efficiency is achieved by providing solutions with multiple resolutions (e.g., obtaining high resolution solutions only for the near future, but allowing coarse approximation for the later stage in the prediction horizon). Full article
(This article belongs to the Special Issue Process Control: Current Trends and Future Challenges)
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223 KiB  
Article
The Effect of Coincidence Horizon on Predictive Functional Control
by John Anthony Rossiter and Robert Haber
Processes 2015, 3(1), 25-45; https://doi.org/10.3390/pr3010025 - 08 Jan 2015
Cited by 39 | Viewed by 4897
Abstract
This paper gives an analysis of the efficacy of PFC strategies. PFC is widely used in industry for simple loops with constraint handling, as it is very simple and cheap to implement. However, the algorithm has had very little exposure in the mainstream [...] Read more.
This paper gives an analysis of the efficacy of PFC strategies. PFC is widely used in industry for simple loops with constraint handling, as it is very simple and cheap to implement. However, the algorithm has had very little exposure in the mainstream literature. This paper gives some insight into when a PFC approach is expected to be successful and, conversely, when one should deploy with caution. Full article
(This article belongs to the Special Issue Process Control: Current Trends and Future Challenges)
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Review

Jump to: Research

628 KiB  
Review
Review on Valve Stiction. Part I: From Modeling to Smart Diagnosis
by Riccardo Bacci Di Capaci and Claudio Scali
Processes 2015, 3(2), 422-451; https://doi.org/10.3390/pr3020422 - 27 May 2015
Cited by 11 | Viewed by 9114 | Retraction
Abstract
Valve stiction is indicated as one of the main problems affecting control loop performance and then product quality. Therefore, it is important to detect this phenomenon as early as possible, distinguish it from other causes, and suggest the correct action to the operator [...] Read more.
Valve stiction is indicated as one of the main problems affecting control loop performance and then product quality. Therefore, it is important to detect this phenomenon as early as possible, distinguish it from other causes, and suggest the correct action to the operator in order to fix it. It is also very desirable to give an estimate of stiction amount, in order to be able to follow its evolution in time to allow the scheduling of valve maintenance or different operations, if necessary. This paper, in two parts, is a review of the state of the art about the phenomenon of stiction from its basic characterization to smart diagnosis, including modeling, detection techniques, quantification, compensation and a description of commercial software packages. In particular, Part I of the study analyzes the most significant works appearing in the recent literature, pointing out analogies and differences among various techniques, showing more appealing features and possible points of weakness. The review also includes an illustration of the main features of performance monitoring systems proposed by major software houses. Finally, the paper gives indications on future research trends and potential advantages for loop diagnosis when additional measurements are available, as in newly designed plants with valve positioners and smart instrumentation. In Part II, performance of some well-established methods for stiction quantification are compared by applications to different industrial datasets. Full article
(This article belongs to the Special Issue Process Control: Current Trends and Future Challenges)
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619 KiB  
Review
Deterministic Performance Assessment and Retuning of Industrial Controllers Based on Routine Operating Data: Applications
by Massimiliano Veronesi and Antonio Visioli
Processes 2015, 3(1), 113-137; https://doi.org/10.3390/pr3010113 - 17 Feb 2015
Cited by 5 | Viewed by 4880
Abstract
Performance assessment and retuning techniques for proportional-integral-derivative (PID) controllers are reviewed in this paper. In particular, we focus on techniques that consider deterministic performance and that use routine operating data (that is, set-point and load disturbance step signals). Simulation and experimental results show [...] Read more.
Performance assessment and retuning techniques for proportional-integral-derivative (PID) controllers are reviewed in this paper. In particular, we focus on techniques that consider deterministic performance and that use routine operating data (that is, set-point and load disturbance step signals). Simulation and experimental results show that the use of integrals of predefined signals can be effectively employed for the estimation of the process parameters and, therefore, for the comparison of the current controller with a selected benchmark. Full article
(This article belongs to the Special Issue Process Control: Current Trends and Future Challenges)
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