Special Issue "Process Control: Current Trends and Future Challenges"

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A special issue of Processes (ISSN 2227-9717).

Deadline for manuscript submissions: closed (31 December 2014)

Special Issue Editor

Guest Editor
Prof. Dr. Gabriele Pannocchia (Website)

Department of Civil and Industrial Engineering-Chemical Engineering Section, University of Pisa, Largo L. Lazzarino, 2, 56126 Pisa, Italy
Phone: +39 050 2217 838
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

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 quarterly 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 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

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

Open AccessArticle Computer-Aided Framework for the Design of Freeze-Drying Cycles: Optimization of the Operating Conditions of the Primary Drying Stage
Processes 2015, 3(2), 406-421; doi:10.3390/pr3020406
Received: 18 December 2014 / Accepted: 16 May 2015 / Published: 25 May 2015
Cited by 2 | PDF Full-text (1457 KB) | HTML Full-text | XML Full-text
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 [...] 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)
Figures

Open AccessArticle An Algorithm for Finding Process Identification Intervals from Normal Operating Data
Processes 2015, 3(2), 357-383; doi:10.3390/pr3020357
Received: 24 January 2015 / Accepted: 23 April 2015 / Published: 6 May 2015
PDF Full-text (396 KB) | HTML Full-text | XML Full-text
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 [...] 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)
Open AccessArticle A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets
Processes 2015, 3(2), 257-285; doi:10.3390/pr3020257
Received: 4 February 2015 / Revised: 15 March 2015 / Accepted: 2 April 2015 / Published: 22 April 2015
Cited by 1 | PDF Full-text (814 KB) | HTML Full-text | XML Full-text
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 [...] 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)
Open AccessArticle Fast Wavelet-Based Model Predictive Control of Differentially Flat Systems
Processes 2015, 3(1), 161-177; doi:10.3390/pr3010161
Received: 19 December 2014 / Revised: 18 February 2015 / Accepted: 26 February 2015 / Published: 11 March 2015
Cited by 1 | PDF Full-text (415 KB) | HTML Full-text | XML Full-text
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 [...] 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)
Open AccessArticle The Effect of Coincidence Horizon on Predictive Functional Control
Processes 2015, 3(1), 25-45; doi:10.3390/pr3010025
Received: 15 October 2014 / Accepted: 12 December 2014 / Published: 8 January 2015
Cited by 4 | PDF Full-text (223 KB) | HTML Full-text | XML Full-text
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 [...] 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)

Review

Jump to: Research

Open AccessReview Review on Valve Stiction. Part I: From Modeling to Smart Diagnosis
Processes 2015, 3(2), 422-451; doi:10.3390/pr3020422
Received: 20 March 2015 / Accepted: 16 May 2015 / Published: 27 May 2015
Cited by 2 | PDF Full-text (628 KB) | HTML Full-text | XML Full-textRetraction
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 [...] 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)
Open AccessReview Deterministic Performance Assessment and Retuning of Industrial Controllers Based on Routine Operating Data: Applications
Processes 2015, 3(1), 113-137; doi:10.3390/pr3010113
Received: 30 December 2014 / Revised: 2 February 2015 / Accepted: 10 February 2015 / Published: 17 February 2015
PDF Full-text (619 KB) | HTML Full-text | XML Full-text
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 [...] 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)

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Type of Paper: Article
Title
: Quality-by Design in Pharmaceuticals Freeze-drying: Optimization of the Operating Conditions of the Primary Drying Stage
Authors
: Davide Fissore and Roberto Pisano
Affiliation
: Dipartimento di Scienza Applicata e Tecnologia – Politecnico di Torino (Italy)
Abstract
: The optimization of the primary drying stage in a pharmaceutical freeze-drying process is a challenging task, as drying time has to be minimized beside preserving product quality. Following the guidelines issued by US-FDA in 2004, and taking advantage of the various Process Analytical Technologies (PAT) that have been proposed in recent years to monitor a freeze-drying process, pharmaceutical manufacturers are moving from a quality-by-testing to a quality-by-design approach. In this framework, model-based tools for process design have been proposed, with the goal to identify the design space of the process. This paper addresses the case of process optimization when processing a product whose dried layer resistance, one of the key model parameters, is affected by the operating conditions. Few experiments are required to identify model parameters, and a simple and effective method to identify the design space (and to optimize the process) in this case is presented.
Keywords
: Freeze-drying; PAT; process design; process optimization; design space

Type of Paper: Article
Title
: Steady-State Identification Analysis of an Industrial Depropanizer Column
Authors
: Franklin D. Rincon 1,2, Galo A. C. Le Roux 2, and Fernando V. Lima 1,*
Affiliations
:1 Department of Chemical Engineering, West Virginia University, P.O. Box 6102, Morgantown, WV 26506, USA; 2 Department of Chemical Engineering, University of S~ao Paulo, Av. Prof. Lineu Prestes, 580, Bloco 18, S~ao Paulo, Brazil
Abstract
: This paper introduces a novel steady-state identi_cation (SSI) method based on the Auto-Regressive model with eXogenous inputs (ARX). This method allows the SSI with reduced tuning by analyzing the identi_ability 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 steady-state identi_cation method is compared to other available techniques, namely the F-like test [1] and a polynomial-based [2] approach. All these methods are implemented for SSI of two di_erent cases. The _rst case corresponds to a literature non-linear CSTR example running at di_erent steady states. Then an industrial case considering real data from a depropanizer column from Petrobras S.A. is analyzed. The robustness of the techniques is veri_ed for the industrial case with di_erent operating conditions.
Keywords
: steady-state; identi_cation; ARX; industrial processes

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