Decision-making for Petrochemical Planning Using Multiobjective and Strategic Tools

Al-Sharrah, Ghanima; Hankinson, G.; Elkamel, A.

doi:10.1205/cherd.05198

Cited by 17 publications

(13 citation statements)

References 26 publications

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“…The deterministic model presented in this study is slightly modified from that of Al-Sharrah et al (2001), Al-Sharrah, Hankinson and Elkamel (2006). The deterministic model presented in this study is slightly modified from that of Al-Sharrah et al (2001), Al-Sharrah, Hankinson and Elkamel (2006).…”

Section: Model Formulationmentioning

confidence: 97%

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Petrochemical Network Planning

Al‐Qahtani

Elkamel

2010

Planning and Integration of Refinery and Petrochemical Operations

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The structure of the petrochemical industry is a highly interactive and complex structure as it involves hundreds of chemicals and processes with products of one process being the feedstocks of many others. For most chemicals, the production route from feedstock to final products is not unique, but includes many possible alternatives. As complicated as it may seem, this structure is comprehensible, at least in a general sense.This chapter explains the general representation of a petrochemical planning model which selects the optimal network from the overall petrochemical superstructure. The system is modeled as a mixed-integer linear programming (MILP) problem and illustrated via a numerical example.

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Section: Model Formulationmentioning

confidence: 97%

“…The case study presented in this book is based on Al-Sharrah, Hankinson and Elkamel (2006). The petrochemical network included 81 processes connecting the production and consumption of 65 chemicals.…”

Section: Illustrative Case Studymentioning

confidence: 99%

Petrochemical Network Planning

Al‐Qahtani

Elkamel

2010

Planning and Integration of Refinery and Petrochemical Operations

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“…A number of quantitative models of the petrochemical industry have been proposed through the years [63–66]. These models are based on representing the network of interconversions between chemical species as mass balance constraints analogous to the constraint‐based representation of a metabolic network.…”

Section: Modeling the Biochemical Industrymentioning

confidence: 99%

Multi‐scale modeling for sustainable chemical production

Zhuang

Bakshi

2013

Biotechnology Journal

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With recent advances in metabolic engineering, it is now technically possible to produce a wide portfolio of existing petrochemical products from biomass feedstock. In recent years, a number of modeling approaches have been developed to support the engineering and decision-making processes associated with the development and implementation of a sustainable biochemical industry. The temporal and spatial scales of modeling approaches for sustainable chemical production vary greatly, ranging from metabolic models that aid the design of fermentative microbial strains to material and monetary flow models that explore the ecological impacts of all economic activities. Research efforts that attempt to connect the models at different scales have been limited. Here, we review a number of existing modeling approaches and their applications at the scales of metabolism, bioreactor, overall process, chemical industry, economy, and ecosystem. In addition, we propose a multi-scale approach for integrating the existing models into a cohesive framework. The major benefit of this proposed framework is that the design and decision-making at each scale can be informed, guided, and constrained by simulations and predictions at every other scale. In addition, the development of this multi-scale framework would promote cohesive collaborations across multiple traditionally disconnected modeling disciplines to achieve sustainable chemical production.

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“…The case study in this chapter is based on Al-Sharrah, Hankinson and Elkamel (2006) and is the same as the example presented in Chapter 4. The case study in this chapter is based on Al-Sharrah, Hankinson and Elkamel (2006) and is the same as the example presented in Chapter 4.…”

Section: Illustrative Case Studymentioning

confidence: 99%

Robust Planning for Petrochemical Networks

Al‐Qahtani

Elkamel

2010

Planning and Integration of Refinery and Petrochemical Operations

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This chapter addresses the planning, design and optimization of a network of petrochemical processes under uncertainty and robust considerations. Similar to the previous chapter, robustness is analyzed based on both model robustness and solution robustness. Parameter uncertainty includes process yield, raw material and product prices, and lower product market demand. The expected value of perfect information (EVPI) and the value of the stochastic solution (VSS) are also investigated to illustrate numerically the value of including the randomness of the different model parameters.8.1

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Decision-making for Petrochemical Planning Using Multiobjective and Strategic Tools

Cited by 17 publications

References 26 publications

Petrochemical Network Planning

Petrochemical Network Planning

Multi‐scale modeling for sustainable chemical production

Robust Planning for Petrochemical Networks

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