Integration of LCA, TEA, Process Simulation and Optimization: A systematic review of current practices and scope to propose a framework for pulse processing pathways

Ferdous, Jannatul; Bensebaa, Farid; Pelletier, Nathan

doi:10.1016/j.jclepro.2023.136804

Cited by 25 publications

(7 citation statements)

References 146 publications

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“…The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method [68,69] was followed to conduct the review of the articles that integrated MOO and MCDM methods. The PRISMA method comprises a 27-item checklist and it has been used in different fields of research [70][71][72][73][74]. There are three stages in the PRISMA method: search strategy, screening criteria, and the extraction and synthesis of data, which are explained in the following sections in detail.…”

Section: Methodology Used For the Systematic Reviewmentioning

confidence: 99%

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

Ferdous,

Bensebaa,

Milani

et al. 2024

Sustainability

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The integration of Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) has gathered significant attention across various scientific research domains to facilitate integrated sustainability assessment. Recently, there has been a growing interest in hybrid approaches that combine MCDM with MOO, aiming to enhance the efficacy of the final decisions. However, a critical gap exists in terms of providing clear methodological guidance, particularly when dealing with data uncertainties. To address this gap, this systematic review is designed to develop a generic decision tree that serves as a practical roadmap for practitioners seeking to perform MOO and MCDM in an integrated fashion, with a specific focus on accounting for uncertainties. The systematic review identified the recent studies that conducted both MOO and MCDM in an integrated way. It is important to note that this review does not aim to identify the superior MOO or MCDM methods, but rather it delves into the strategies for integrating these two common methodologies. The prevalent MOO methods used in the reviewed articles were evolution-based metaheuristic methods. TOPSIS and PROMETHEE II are the prevalent MCDM ranking methods. The integration of MOO and MCDM methods can occur either a priori, a posteriori, or through a combination of both, each offering distinct advantages and drawbacks. The developed decision tree illustrated all three paths and integrated uncertainty considerations in each path. Finally, a real-world case study for the pulse fractionation process in Canada is used as a basis for demonstrating the various pathways presented in the decision tree and their application in identifying the optimized processing pathways for sustainably obtaining pulse protein. This study will help practitioners in different research domains use MOO and MCDM methods in an integrated way to identify the most sustainable and optimized system.

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Section: Methodology Used For the Systematic Reviewmentioning

confidence: 99%

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

Ferdous,

Bensebaa,

Milani

et al. 2024

Sustainability

Self Cite

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“…is vast and a huge number of cations and anions is available for potentially enabling the synthesis of millions of ILs, 30 the assurance for achieving the desired criteria was relatively low, even when performing long and costly experimental screenings and pilot plant tests. 31 Process simulation�a professional computational tool decisively involved in the conceptual design and basic engineering stages for developing chemical processes� 32 has substantially contributed to the main challenges of IL research. Thus, steady-state process simulations have supported the computational synthesis and analysis of new processes comprising the use of ILs, thereby enabling the mass−energy balance, equipment design, and evaluation of the energy requirements and economics of new processes at the industrial scale.…”

Section: Introductionmentioning

confidence: 99%

“…Process simulationa professional computational tool decisively involved in the conceptual design and basic engineering stages for developing chemical processes has substantially contributed to the main challenges of IL research. Thus, steady-state process simulations have supported the computational synthesis and analysis of new processes comprising the use of ILs, thereby enabling the mass–energy balance, equipment design, and evaluation of the energy requirements and economics of new processes at the industrial scale. − In addition, process simulations have enabled the scientific community to perform the difficult task of the selection of ILs with favorable solvent/catalyst behaviors by combining the specifications of a concrete operation with the most relevant well-known IL constraints, namely, the melting point, thermal stability, viscosity, and price. − For example, although ILs are thermally stable, the design of the IL regeneration operation was drastically conditioned by the temperature range allowed for each IL, which ultimately determined the IL selection or required an operating vacuum .…”

Section: Introductionmentioning

confidence: 99%

Process Simulation and Optimization on Ionic Liquids

Palomar,

Lemus,

Navarro

et al. 2024

Chem. Rev.

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Ionic liquids (ILs) are promising alternative compounds that enable the development of technologies based on their unique properties as solvents or catalysts. These technologies require integrated product and process designs to select ILs with optimal process performances at an industrial scale to promote cost-effective and sustainable technologies. The digital era and multiscale research methodologies have changed the paradigm from experiment-oriented to hybrid experimental−computational developments guided by process engineering. This Review summarizes the relevant contributions (>300 research papers) of process simulations to advance IL-based technology developments by guiding experimental research efforts and enhancing industrial transferability. Robust simulation methodologies, mostly based on predictive COSMO-SAC/RS and UNIFAC models in Aspen Plus software, were applied to analyze key IL applications: physical and chemical CO 2 capture, CO 2 conversion, gas separation, liquid−liquid extraction, extractive distillation, refrigeration cycles, and biorefinery. The contributions concern the IL selection criteria, operational unit design, equipment sizing, technoeconomic and environmental analyses, and process optimization to promote the competitiveness of the proposed IL-based technologies. Process simulation revealed that multiscale research strategies enable advancement in the technological development of IL applications by focusing research efforts to overcome the limitations and exploit the excellent properties of ILs.

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“…10,11 Recently reported biobased impurities usually lack a properties database, and obtaining measurement data through experiments often requires a substantial amount of resources. Aspen, a commonly used commercial tool for developing bioprocess models, 12 has a comprehensive database of pure component properties. 13 However, for pure components for which there is no existing database, the properties need to be predicted using a property prediction algorithm called the property constant estimation system (PCES) method.…”

Section: Introductionmentioning

confidence: 99%

“…Aspen, a commonly used commercial tool for developing bioprocess models, 12 has a comprehensive database of pure component properties. 13 However, for pure components for which there is no existing database, the properties need to be predicted using a property prediction algorithm called the property constant estimation system (PCES) method.…”

Section: Introductionmentioning

confidence: 99%

Python-Based Algorithm for Estimating the Parameters of Physical Property Models for Substances Not Available in Database

Lee,

Won,

Kim

2024

ACS Omega

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An in-house Python-based algorithm was developed using simplified molecular-input line-entry specification (SMILES) strings and a dipole moment for estimating the normal boiling point, critical properties, standard enthalpy, vapor pressure, liquid molar volume, enthalpy of vaporization, heat capacity, viscosity, thermal conductivity, and surface tension of molecules. Normal boiling point, critical properties, and standard enthalpy were estimated by using the Joback group contribution method. Vapor pressure, liquid molar volume, enthalpy of vaporization, heat capacity, and surface tension were estimated by using the Riedel model, Gunn−Yamada model, Clausius−Clapeyron equation, Joback group contribution method, and Brock−Bird model, respectively. Viscosities of liquid and gas were estimated by using the Letsou−Stiel model and the Chapman−Enskog−Brokaw model, respectively. Thermal conductivities of liquid and gas were estimated by using the Sato−Riedel model and Stiel−Thodos model, respectively. Dipole moment was calculated through molecular dynamics simulation using the MMFF94 force field, performed with Avogadro software. A case study was conducted with dihydro-2-methyl-3-furanone (DHMF), 2-furaldehyde diethyl acetal (FDA), 1,1-diethoxy-3-methyl butane (DEMB), glutathione (GSH), vitamin B5 (VITB5), homocysteine (HCYS), and O-acetyl-L-homoserine (AH), which are not present in the existing property database. Cross-validation indicated that the developed Python-based algorithm provided pure component model parameters nearly identical with those obtained with the Aspen Property Constant Estimation System (PCES) method, except for the enthalpy of vaporization. The parameters for estimating the enthalpy of vaporization using the current Python-based algorithm accurately represented the behavior of the actual substances, as determined using the Clausius−Claperyon equation. This Pythonbased algorithm provides a detailed and clear reference for estimating pure property parameters.

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Integration of LCA, TEA, Process Simulation and Optimization: A systematic review of current practices and scope to propose a framework for pulse processing pathways

Cited by 25 publications

References 146 publications

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

Process Simulation and Optimization on Ionic Liquids

Python-Based Algorithm for Estimating the Parameters of Physical Property Models for Substances Not Available in Database

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