Special Issue "Process Control: Current Trends and Future Challenges"
A special issue of Processes (ISSN 2227-9717).
Deadline for manuscript submissions: 31 December 2014
Dr. Gabriele Pannocchia
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; optimal drug administration using advanced control algorithms; closed-loop identification and performance monitoring; inferential control systems; process modeling, simulation and optimization
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
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. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts. 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.
- 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
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  and a polynomial-based  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
Last update: 29 September 2014