A multi-layered view of chemical and biochemical engineering

Gani, Rafiqul; Bałdyga, J.; Biscans, Béatrice; Brunazzi, Elisabetta; Charpentier, Jean‐Claude; Drioli, Enrico; Feise, Hermann J.; Furlong, Andrew; Geem, Kevin Van; Hemptinne, Jean-Charles de; Kate, Antoon J.B. ten; Kontogeorgis, Georgios M.; Manenti, Flavio; Marin, Guy; Mansouri, Seyed Soheil; Piccione, Patrick M.; Barbosa‐Póvoa, Ana Paula; Rodrigo, Manuel A.; Sarup, Bent; Sørensen, Eva; Udugama, Isuru A.; Woodley, John M.

doi:10.1016/j.cherd.2020.01.008

Cited by 68 publications

(30 citation statements)

References 58 publications

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“…Differing from the original database and previous studies, a safer and lower toxic crystallization solvent and a significantly improved k L reaction solvent are designed in the crystallization and reaction case studies, respectively, confirming the wide applicability and effectiveness of the optimization‐based framework for solvent design. Experimental verifications are being planned as future research to establish the designed solvents as well as to develop integrated solution approaches combining model‐based solution approaches with experiments such that novel and sustainable solutions can be obtained faster and at lower cost 31 . For other prospects, the applications of MLAC method can be extended to drug molecule design with more elements (e.g., halogen), and other molecular properties that the GC method is not able to predict well (e.g., dielectric constant).…”

Section: Resultsmentioning

confidence: 99%

Machine learning‐based atom contribution method for the prediction of surface charge density profiles and solvent design

et al. 2020

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Solvents are widely used in chemical processes. The use of efficient model‐based solvent selection techniques is an option worth considering for rapid identification of candidates with better economic, environment and human health properties. In this paper, an optimization‐based MLAC‐CAMD framework is established for solvent design, where a novel machine learning‐based atom contribution method is developed to predict molecular surface charge density profiles (σ‐profiles). In this method, weighted atom‐centered symmetry functions are associated with atomic σ‐profiles using a high‐dimensional neural network model, successfully leading to a higher prediction accuracy in molecular σ‐profiles and better isomer identifications compared with group contribution methods. The new method is integrated with the computer‐aided molecular design technique by formulating and solving a mixed‐integer nonlinear programming model, where model complexities are managed with a decomposition‐based strategy. Finally, two case studies involving crystallization and reaction are presented to highlight the wide applicability and effectiveness of the MLAC‐CAMD framework.

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Section: Resultsmentioning

confidence: 99%

Machine learning‐based atom contribution method for the prediction of surface charge density profiles and solvent design

et al. 2020

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“…These advantages not only attract industrial perspectives and applications 6 but also make RD a suitable intensified process to combat ever-increasing worldwide energy demand, which is expected to increase about 3.5 times (7 times increase in electricity demand) to sustain the ever-increasing population. 7 The potential of RD technology, however, has not yet been fully tapped, and there is still ongoing research to improve it further by other means, for example, ultrasound or microwave-assisted RD, 8 use of high-gravity fields (HiGee), 9 and/or addressing issues such as operability and control. 10 There have been more than 150 successful industrial applications of RD technology, 11 for example, for selective hydrogenation of mixed hydrocarbons, selective desulfurization of mid catalytic naphtha, and isomerization of n-olefins to iso-olefins.…”

Section: Introductionmentioning

confidence: 99%

“…RD improves the productivity and selectivity by primarily simultaneous removal of products from reactants as well as suppressing side reactions, 2 reduces capital cost as well as the need for solvents, 3 avoids/degrades azeotropes, 4 reduces energy usage by using the heat of exothermic reaction in situ for the vaporization of the liquid, resulting in lower CO 2 emission and less waste, 5 among others. These advantages not only attract industrial perspectives and applications 6 but also make RD a suitable intensified process to combat ever‐increasing worldwide energy demand, which is expected to increase about 3.5 times (7 times increase in electricity demand) to sustain the ever‐increasing population 7 . The potential of RD technology, however, has not yet been fully tapped, and there is still ongoing research to improve it further by other means, for example, ultrasound or microwave‐assisted RD, 8 use of high‐gravity fields (HiGee), 9 and/or addressing issues such as operability and control 10 .…”

Section: Introductionmentioning

confidence: 99%

Integrated design and control of reactive distillation processes using the driving force approach

et al. 2021

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Superior controllability of reactive distillation (RD) systems, designed at the maximum driving force (design-control solution) is demonstrated in this article. Binary or multielement single or double feed RD systems are considered. Reactive phase equilibrium data, needed for driving force analysis and design of the RD system, is generated through an in-house property prediction tool. Rigorous steady-state simulation is carried out in ASPEN plus in order to verify that the predefined design targets and dynamics are met. A multiobjective performance function is employed to evaluate the performance of the RD system in terms of energy consumption, sustainability metrics (total CO 2 footprint), and control performance. Controllability of the designed system is evaluated using indices like the relative gain array (RGA) and Niederlinski index (N I ), to evaluate the degree of loop interaction, as well as through dynamic simulations using proportional-integral (PI) controllers and model predictive controllers (MPC). The design-control of the RD systems corresponding to other alternative designs that do not take advantage of the maximum driving force is also investigated. The analysis shows that the RD designs at the maximum driving force exhibit enhanced controllability and lower carbon footprint than the alternative RD designs.

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“…Compared with epidemiology, dynamic optimization theory in chemical and biochemical engineering has a long and consolidated history. 15 Manenti et al 16 proposed an analogy between the simple SIRD model and the behavior of chemical reactors by assuming each compartment to be molecules of chemical compounds. The corresponding predictive model based on chemical and physical considerations has been validated with data from different regions, which already underwent complete infection dynamics.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Control of COVID-19 Transmission from a Perspective of Polymerization Reaction Dynamics

Zhang

Liao

Sun

et al. 2021

Ind. Eng. Chem. Res.

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Due to the serious economic losses and deaths caused by COVID-19, the modeling and control of such a pandemic has become a hot research topic. This paper finds an analogy between a polymerization reaction and COVID-19 transmission dynamics, which will provide a novel perspective to optimal control measures. Susceptible individuals, exposed people, infected cases, recovered population, and the dead can be assumed to be specific molecules in the polymerization system. In this paper, a hypothetical polymerization reactor is constructed to describe the transmission of an epidemic, and its kinetic parameters are regressed by the least-squares method. The intensity of social distancing u is considered to the mixing degree of the reaction system, and contact tracing and isolation ρ can be regarded as an external circulation in the main reactor to reduce the concentration of active species. Through these analogies, this model can predict the peak infection, deaths, and end time of the epidemic under different control measures to support the decision-making process. Without any measures (u = 1.0 and ρ = 0), more than 90% of the population would be infected. It takes several years to complete herd immunity by nonpharmacological intervention when the proportion of deaths is limited to less than 5%. However, vaccination can reduce the time to tens to hundreds of days, which is related to the maximum number of vaccines per day.

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A multi-layered view of chemical and biochemical engineering

Cited by 68 publications

References 58 publications

Machine learning‐based atom contribution method for the prediction of surface charge density profiles and solvent design

Machine learning‐based atom contribution method for the prediction of surface charge density profiles and solvent design

Integrated design and control of reactive distillation processes using the driving force approach

Modeling and Control of COVID-19 Transmission from a Perspective of Polymerization Reaction Dynamics

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