Mitigation of energy intensity of an industrial off-gas cleaning system using process engineering solutions

Araújo, Antônio Carlos Brandão de; Brito, Romildo Pereira; Shang, Helen

doi:10.1016/j.jclepro.2009.05.014

Cited by 11 publications

(21 citation statements)

References 17 publications

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“…The initiative was inspired by previous work (Araujo and Skogestad, 2007a;Araujo and Skogestad, 2007b;Baldea, Araujo, and Skogestad, 2008;Araujo and Skogestad, 2008;Araujo and Shang, 2009a;Araujo and Shang, 2009b;Araujo and Shang, 2009c) applying the self-optimizing control to chemical process systems, confirming the efficiency of the method. However, these studies used independent and unrelated pieces of software such as Matlab ® , AspenPlus ® and Microsoft Excel ® in a decentralized fashion via extremely tedious computations and data manipulation, which of course is only permissible in an academic environment, never to be used by practitioner process engineers A friendly interface for problem configuration, storage of data, and display of results, along with very efficient routines to compute the gain matrix (first order information), second order information (Hessian of cost function), rank of matrices, and subsets of variables that maximize the minimum singular value of submatrices (bidirectional branch-and-bound), make this proposition unique and, to the authors' knowledge, never attempted before in the plantwide control community.…”

Section: Discussionmentioning

confidence: 78%

“…Many authors have reported results of the use of the selfoptimizing control technology that ensure the efficiency of the method applied to a great variety of test-bed examples (e.g., Jensen and Skogestad, 2007a;Jensen and Skogestad, 2007b;Hori and Skogestad, 2007a,b;Lersbamrungsuk et al, 2008;Jagtap et al, 2011;Panahi and Skogestad, 2012;Gera et al, 2013;Jaschke and Skogestad, 2014;Khanam et al, 2014;Graciano et al, 2015). Moreover, application of this technology to different case studies using process simulators has also been reported in the literature (Araujo and Skogestad, 2007a;Araujo and Skogestad, 2007b;Baldea, Araujo, and Skogestad, 2008;Araujo and Skogestad, 2008;Araujo and Shang, 2009a;Araujo and Shang, 2009b;Araujo and Shang, 2009c), and the results showed the feasibility and ease of implementation of the self-optimizing control procedure in such instances. However, much effort and time is usually expended doing many mathematical computations using different pieces of software quite alike from one another.…”

Section: Introductionmentioning

confidence: 93%

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Development and application of an automatic tool for the selection of control variables based on the self-optimizing control methodology

Silva

Villar

Costa

et al. 2017

Braz. J. Chem. Eng.

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-Rules for control structure design for industrial processes have been extensively proposed in the literature. Some model-based methodologies have a sound mathematical basis, such as the self-optimizing control technology. The procedure can be applied with the aid of available commercial simulators, e.g., PRO/II TM and AspenPlus®, from which the converging results are obtained more suitably for industrial applications, lessening the effort needed to build an appropriate mathematical model of the plant. Motivated by this context, this work explores the development and application of a tool designed to automatically generate near-optimal controlled structures for process plants based on the self-optimizing control technology. The goal is to provide a means to facilitate the way possible arrangements of controlled variables are generated. Using the local minimum singular value rule supported by a modified version of a branch-and-bound algorithm, the best sets of candidate controlled variables can be identified that minimize the loss between real optimal operation and operation under constant set-point policy. A case study consisting of a deethanizer is considered to show the main features of the proposed tool. The conclusion indicates the feasibility of merging complex theoretical contents within the framework of a user-friendly interface simple enough to generate control structures suitable for real world implementation.

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Section: Discussionmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 93%

Development and application of an automatic tool for the selection of control variables based on the self-optimizing control methodology

Silva

Villar

Costa

et al. 2017

Braz. J. Chem. Eng.

View full text Add to dashboard Cite

show abstract

“…It is not supposed to be an exhaustive discussion on the subject; instead, we give the necessary elements for a proper understanding of the main ideas lying behind the method. Indeed, this plantwide procedure has been successfully applied to other processes as described in various publications in the field. − , Indeed, a review of this methodology and others, more heuristics as well as applications thereof can be found in Rangaiah and Kariwala …”

Section: Controlled Variable Selection Methodologymentioning

confidence: 99%

“…Indeed, this plantwide procedure has been successfully applied to other processes as described in various publications in the field. [31][32][33][34][35][36]12 Indeed, a review of this methodology and others, more heuristics as well as applications thereof can be found in Rangaiah and Kariwala. 37 The methodology is mainly based on the first four steps, known as "top-down analysis", of the more general procedure described in Skogestad, 38 where economic variable selection is the key issue.…”

Section: Controlled Variable Selection Methodologymentioning

confidence: 99%

Sensitivity Analysis of Optimal Operation of an Activated Sludge Process Model for Economic Controlled Variable Selection

Araújo

Gallani

Mulas

et al. 2013

Ind. Eng. Chem. Res.

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This paper describes a systematic sensitivity analysis of optimal operation conducted on an activated sludge process model based on the test-bed benchmark simulation model no. 1 (BSM1) and the activated sludge model no. 1 (ASM1). The objective is to search for a control structure that leads to optimal economic operation, while promptly rejecting disturbances at lower layers in the control hierarchy avoiding thus violation of the more important regulation constraints on effluent discharge. We start by optimizing a steady-state nonlinear model of the process. Here, a new steady-state secondary settler mathematical model is developed based on the theory of partial differential equations applied to the conservation law with discontinuous fluxes. The resulting active constraints must be chosen as economic controlled variables. These are the effluent ammonia from the bioreaction section and the final effluent total suspended solids at their respective upper limits, in addition to the internal recycle flow rate at its lower bound. The remaining degrees of freedom need to be fulfilled, and we use several local (linear) sensitivity methods to find a set of unconstrained controlled variables that minimizes the loss between actual and optimal operation; particularly we choose to control linear combinations of readily available measurements so to minimize the effect of disturbances and implementation errors on the optimal static performance of the plant. It is expected that the proposed methodology and results obtained therein can be used in practice as general rules-of-thumb to be tested in actual wastewater treatment plants of the kind discussed in this paper.

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“…For example, SOC and IFSH have been applied to HDA, styrene, and ammonia synthesis processes. − Application of PWC methodologies to industrial processes is of interest to test the effectiveness of the proposed methodology and to compare different PWC system design methodologies. From PWC perspective, apart from HDA, styrene, and ammonia synthesis processes, researchers have studied several other processes such as biodiesel process. − …”

Section: Introductionmentioning

confidence: 99%

Plant-Wide Control System Design of an Alkylation Process Using Integrated Framework of Simulation, Heuristics, and Optimization

Tripathi

Vasudevan

Rangaiah

2013

Ind. Eng. Chem. Res.

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In recent years, increasing concern of process economics, strict product specifications, and stringent safety and environmental regulations has resulted in increased use of material recycle and energy integration in chemical process plants. These highly integrated plants need effective control systems for smooth and optimal operation. This has led researchers to propose plant-wide control (PWC) system design methodologies. The present contribution considers the application of one such recently proposed methodology, the integrated framework of simulation, heuristics, and optimization (IFSHO) [Vasudevan, S.; Rangaiah, G. P. Integrated Framework Incorporating Optimization for Plant-Wide Control of Industrial Processes. Ind. Eng. Chem. Res. 2011, 50, 8122−8137]. This framework is applied to the autorefrigerated alkylation process for the production of high-octane C8 components, which is important in petroleum refining. The step-by-step application of IFSHO is discussed. The performance of the resulting control structure is analyzed and compared to that of the earlier control structure [Luyben, W. L. Design and Control of an Autorefrigerated Alkylation Process. Ind. Eng. Chem. Res. 2009, 48, 11081−11093], using several performance assessment criteria.

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Mitigation of energy intensity of an industrial off-gas cleaning system using process engineering solutions

Cited by 11 publications

References 17 publications

Development and application of an automatic tool for the selection of control variables based on the self-optimizing control methodology

Development and application of an automatic tool for the selection of control variables based on the self-optimizing control methodology

Sensitivity Analysis of Optimal Operation of an Activated Sludge Process Model for Economic Controlled Variable Selection

Plant-Wide Control System Design of an Alkylation Process Using Integrated Framework of Simulation, Heuristics, and Optimization

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