Control and operations: when does controllability equal profitability?

Edgar, Thomas F.

doi:10.1016/j.compchemeng.2004.07.013

Cited by 51 publications

(25 citation statements)

References 10 publications

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“…The biotechnological industry is an example of this kind of production. A large portion of bioreactors operates in batch and fed‐batch mode because of their advantages over continuous processes, such as adjustability of batch time to meet specifications and repetitive and slow nature of batch processes 2. Additionally, fed‐batch is believed to outperform batch processing in fermentation processes 3.…”

Section: Introductionmentioning

confidence: 99%

Self‐tuning run to run optimization of fed‐batch processes using unfold‐PLS

2007

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in Wiley InterScience (www.interscience.wiley.com).Unfold Partial-Least Squares (u-PLS) is a modeling method successfully applied to batch-process monitoring and end quality prediction. This method is integrated in a self-tuning optimization algorithm, based on extremum-seeking control. The optimization is driven by the gradient obtained by means of an adaptive u-PLS model. Since this is an empirical model, no first-principles based knowledge of the process is necessary. Heuristic rules are used to constrain the gradient taking into account nonlinearity and unknown causes of variability. Extensions to model the variability in initial conditions, to optimize several performance indices, and to handle inequality constraints are presented. The optimization algorithm is tested on a complex comprehensive simulated-process model for the fed-batch cultivation of Sacharomyces cerevisiae. Results show the performance and versatility of the proposed approach, as well as its robustness to process changes.

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Section: Introductionmentioning

confidence: 99%

Self‐tuning run to run optimization of fed‐batch processes using unfold‐PLS

2007

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“…Surveys such as those commissioned by Suncor Extraction Energy, Canada [1] (7 per cent increase product yield), Hickson & Welch PLC, UK [2] (30 per cent energy reduction), DuPont Company [3] (7-15 per cent operating cost savings), ICI [4,5] (7 per cent operating cost saving), and the Warren Centre study [6,7] (2-6 per cent operating cost saving averaged across several studied plants) have consistently showed that advanced control strategies will lead to more sustainable manufacturing, improved bottomline returns, efficiency gains, improved raw-material yields, improved safety, and reduced environmental impact [8]. In spite of this, the worldwide instances of the application of advanced process control is estimated to be less than 6000 [9].…”

Section: Introductionmentioning

confidence: 98%

Improved Profitability of a Pulping Mill through Progressive Controller Structure Expansion

Nobakhti

2010

Proceedings of the Institution of Mechanical Engineers, Part I:

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It has been well documented that multivariate systems will benefit from application of advanced control strategies. The application of these methods has thus far been hindered for a variety of reasons which range from complexity of the design methodologies to the cost associated with implementation of these solutions. This paper shows that there exist alternative means by which existing control strategies of a multivariate system could be extended, thereby leading to increased efficiency, without the need to remove and implement a new control system installation. A realistic model of an entire pulping mill is used in this paper to show that by careful selection of only a few additional control loops which are added to the existing structure, sizeable profitability and operational gains may be achieved.

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“…Alternative process plants designs have different control performance. This will depend on aspects of the plant units and their configuration, creating both unit and system holdups and sensitivities, and on the type of control exercised (Morari, 1983;Edgar, 2004). Strutzel and Bogle (2016) presented a design approach using the Economic MPC Optimisation (EMOP) index as a tool for the assessment of different process designs based on a monetised measure of the control effort required in a number of scenarios.…”

Section: Introductionmentioning

confidence: 99%

A simple multi-model prediction method

Strutzel

Bogle

2018

Chemical Engineering Research and Design

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The present work introduces a new multi-model state-space formulation called simultaneous multi-linear prediction (SMLP), which is suitable for systems with significant gain variation due to nonlinearity. Standard multi-model formulations usually make use of a partitioned state-space, i.e., a state-space that is divided into regions to shift parameters of the state update equation according to the current location of the state, with a view to having a better approximation of a nonlinear plant on each region. This multi-model framework, also known as linear hybrid systems framework, makes use of different boundaries or partition rules concepts, which vary from systems of linear inequalities, propositional logic rules, or a combination of these. This standard approach inevitably introduces discontinuities in the output prediction as the state update equation parameters shift noticeably. Instead, the SMLP is built by defining and updating multiple states simultaneously, thus eliminating the need for partitioning the state-input space into regions and associating with each region a different state update equation. Each state's contribution to the overall output is obtained according to the relative distance between their identification (or linearisation) point and the current operating point, in addition to a set of parameters obtained through regression analysis. Unlike the methods belonging to the hybrid systems framework, no discontinuities are introduced in the output prediction

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Control and operations: when does controllability equal profitability?

Cited by 51 publications

References 10 publications

Self‐tuning run to run optimization of fed‐batch processes using unfold‐PLS

Self‐tuning run to run optimization of fed‐batch processes using unfold‐PLS

Improved Profitability of a Pulping Mill through Progressive Controller Structure Expansion

A simple multi-model prediction method

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