Smart plant operations: Vision, progress and challenges

Christofides, Panagiotis D.; Davis, James F.; El‐Farra, Nael H.; Clark, D. M.; Harris, K.; Gipson, Jerry N.

doi:10.1002/aic.11320

Cited by 148 publications

(63 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is a significant step away from traditional continuous process operations which focused on steady state designs. However, there has been a recent trend towards flexible or "smart" plants [129,130] , in which processing operations are integrated with business needs and are made more agile allowing for swiftly moving business needs to be met. This latter point about smart plant operations is intimated related to distributed and networked control as the distribution of sensors, actuators and decision making is common to both.…”

Section: Flexible Manufacturing and Integration With Business Objectivesmentioning

confidence: 99%

Distributed control of chemical process networks

Tippett

Bao

2015

Int. J. Autom. Comput.

View full text Add to dashboard Cite

Abstract:In this paper, we present a review of the current literature on distributed (or partially decentralized) control of chemical process networks. In particular, we focus on recent developments in distributed model predictive control, in the context of the specific challenges faced in the control of chemical process networks. The paper is concluded with some open problems and some possible future research directions in the area.

show abstract

Section: Flexible Manufacturing and Integration With Business Objectivesmentioning

confidence: 99%

Distributed control of chemical process networks

Tippett

Bao

2015

Int. J. Autom. Comput.

View full text Add to dashboard Cite

show abstract

“…[1] As a result, today's process manufacturing enterprises require new solutions to survive and grow in this highly competitive international marketplace. [2] To address these challenges, plant-wide optimization has attracted considerable interest in recent years, [3][4][5] and has been recognized as a major goal in the processing industries. [6][7][8] However, many challenges still emerge in plant-wide optimization in dealing with the existence of numerous design variables and constraints during the solution phase.…”

Section: Introductionmentioning

confidence: 99%

A new plant‐wide optimization method and its application to hydrometallurgy process

Yuan

Wang

et al. 2016

Can J Chem Eng

View full text Add to dashboard Cite

This work aims to find an efficient and fast solution for the problem of plant-wide optimization of a large-scale industrial process. The process is composed of a series of unit processes with coupling relationships between them, which leads to the problem of optimizing a large number of design variables and constraints. Therefore, the solution to the optimization problem is time-consuming and the global optimum is obtained with a low probability. To efficiently solve the problem, a new plant-wide optimization method is developed that decomposes the complicated optimization problem into several easier solvable sub-problems based on the coupling relationships between unit processes. Two metrics, constraints coupling and indices coupling, are defined in order to measure the extent of a coupling relationship between pairs of unit processes. Then, the global optimum is found by successively solving the sub-problems. Finally, the hydrometallurgy process of a gold smelter is demonstrated to evaluate the performance of the proposed method.

show abstract

“…Chemical process designers almost always intend to maximize the profits, while respecting environmental and safety regulations (Christofides and Davis 2007). However, their economic intentions often drive the chemical processes to operate towards their operational boundaries, such as maximization of the throughput, or minimization of wastes (Narraway and Perkins 1993;Edgar 2004).…”

Section: Introductionmentioning

confidence: 99%

Optimal selection of control structure using a steady-state inversely controlled process model

Sharifzadeh

Thornhill

2012

Computers & Chemical Engineering

View full text Add to dashboard Cite

The profitability of chemical processes strongly depends on their control systems. The design of a control system involves selection of controlled and manipulated variables, known as control structure selection. Systematic generation and screening alternative control structures requires optimization. However, the size of such an optimization problem is much larger when candidate controllers and their parameters are included and it rapidly becomes intractable. This paper presents a novel optimization framework using the notion of perfect control, which disentangles the complexities of the controllers. This framework reduces the complexity of the problem while ensuring controllability. In addition, the optimization framework has a goal-driven multi-objective function and requires only a steady-state inverse process model. Since dynamic degrees of freedom do not appear in a steady-state analysis, engineering insights are employed for developing the inventory control systems. The proposed optimization framework was demonstrated in a case study of an industrial distillation train.

show abstract

Smart plant operations: Vision, progress and challenges

Cited by 148 publications

References 38 publications

Distributed control of chemical process networks

Distributed control of chemical process networks

A new plant‐wide optimization method and its application to hydrometallurgy process

Optimal selection of control structure using a steady-state inversely controlled process model

Contact Info

Product

Resources

About