Publications
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Jonuzaj S, Watson OL, Ottoboni S, et al., 2020, Computer-aided Solvent Mixture Design for the Crystallisation and Isolation of Mefenamic Acid, Editors: Pierucci, Manenti, Bozzano, Manca, Publisher: ELSEVIER SCIENCE BV, Pages: 649-654
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- Citations: 3
Jonuzaj S, Cui J, Adjiman CS, 2019, Computer-aided design of optimal environmentally benign solvent-based adhesive products, Computers and Chemical Engineering, Vol: 130, ISSN: 0098-1354
The manufacture of improved adhesive products that meet specified target properties has attracted increasing interest over the last decades. In this work, a general systematic methodology for the design of optimal adhesive products with low environmental impact is presented. The proposed approach integrates computer-aided design tools and Generalised Disjunctive Programming (GDP), a logic-based framework, to formulate and solve the product design problem. Key design decisions in product design (i.e., how many components should be included in the final product, which active ingredients and solvent compounds should be used and in what proportions) are optimised simultaneously. This methodology is applied to the design of solvent-based acrylic adhesives, which are commonly used in construction. First, optimal product formulations are determined with the aim to minimize toxicity. This reveals that number of components in the product formulation does not correlate with performance and that high performance can be achieved by investigating different number of components as well as by optimising all ingredients simultaneously rather than sequentially. The relation between two competing objectives (product toxicity and concentration of the active ingredient) is then explored by obtaining a set of Pareto optimal solutions. This leads to significant trade-offs and large areas of discontinuity driven by discrete changes in the list of optimal ingredients in the product.
Jonuzaj S, Gupta A, Adjiman CSJ, 2018, The design of optimal mixtures from atom groups using Generalized Disjunctive Programming, Computers and Chemical Engineering, Vol: 116, Pages: 401-421, ISSN: 1873-4375
A comprehensive computer-aided mixture/blend design methodology for formulating a gen-eral mixture design problem where the number, identity and composition of mixture constituentsare optimized simultaneously is presented in this work. Within this approach, Generalized Dis-junctive Programming (GDP) is employed to model the discrete decisions (number and identitiesof mixture ingredients) in the problems. The identities of the components are determined bydesigning molecules from UNIFAC groups. The sequential design of pure compounds and blends,and the arbitrary pre-selection of possible mixture ingredients can thus be avoided, making itpossible to consider large design spaces with a broad variety of molecules and mixtures. Theproposed methodology is first applied to the design of solvents and solvent mixtures for max-imising the solubility of ibuprofen, often sought in crystallization processes; next, antisolventsand antisolvent mixtures are generated for minimising the solubility of the drug in drowning outcrystallization; and finally, solvent and solvent mixtures are designed for liquid-liquid extraction.The GDP problems are converted into mixed-integer form using the big-M approach. Integercuts are included in the general models leading to lists of optimal solutions which often containa combination of pure and mixed solvents.
Cui J, Jonuzaj S, Adjiman C, 2018, A Comprehensive Approach for the Design of Solvent-based Adhesive Products using Generalized Disjunctive Programming, Amsterdam, Netherlands, 13th International Symposium on Process Systems Engineering (PSE 2018), Publisher: Elsevier B.V., ISSN: 1570-7946
In this work, we present a comprehensive and systematic methodology for the design of optimal adhesive products within the computer-aided product design (CAPD) framework. In the proposed approach, the optimal number, identities and compositions of active ingredients and solvents in the final product are determined simultaneously. Generalised Disjunctive Programming (GDP) is employed to formulate the main design decisions of the problem (i.e., how many ingredients should be included, which active ingredients and solvents compounds should be used and in what proportions). The design methodology has been applied to identifying cheap and environmentally friendly acrylic adhesives, which are commonly used in construction.
Jonuzaj S, Adjiman CSJ, 2016, Designing optimal mixtures using generalized disjunctive programming: Hull relaxations, Chemical Engineering Science, Vol: 159, Pages: 106-130, ISSN: 1873-4405
A general modeling framework for mixture design problems, which integrates Generalized Disjunctive Programming (GDP) into the Computer-Aided Mixture/blend Design (CAMbD) framework, was recently proposed (S. Jonuzaj, P.T. Akula, P.-M. Kleniati, C.S. Adjiman, 2016. AIChE Journal 62, 1616–1633). In this paper we derive Hull Relaxations (HR) of GDP mixture design problems as an alternative to the big-M (BM) approach presented in this earlier work. We show that in restricted mixture design problems, where the number of components is fixed and their identities and compositions are optimized, BM and HR formulations are identical. For general mixture design problems, where the optimal number of mixture components is also determined, a generic approach is employed to enable the derivation and solution of the HR formulation for problems involving functions that are not defined at zero (e.g., logarithms). The design methodology is applied successfully to two solvent design case studies: the maximization of the solubility of a drug and the separation of acetic acid from water in a liquid-liquid extraction process. Promising solvent mixtures are identified in both case studies. The HR and BM approaches are found to be effective for the formulation and solution of mixture design problems, especially via the general design problem.
Jonuzaj S, Adjiman CS, 2016, A Convex Hull Formulation for the Design of Optimal Mixtures, 26th European Symposium on Computer Aided Process Engineering, Publisher: Elsevier, Pages: 2325-2330, ISSN: 1570-7946
The design of mixtures plays an important role in improving process and product performancebut is challenging because it requires finding the optimal number, identities and compositions ofmixture components and using nonlinear property models. To address this, a general modelingframework for mixture design problems is presented. It integrates Generalized Disjunctive Programming(GDP) into Computer-Aided Mixture/blend Design via Hull Reformulation (HR). Thedesign methodology is applied successfully to a case study involving solid-liquid equilibrium calculationsto find an optimal solvent mixture that dissolves ibuprofen. The results show that theproposed GDP-based approach appears very promising for the design of mixture problems. TheHR approach is used to solve mixture problems successfully. Its overall computational efficiencyis found to be better than that of Big-M approach. Numerical difficulties arising from the absenceof components in the final mixture can be avoided, leading to computationally efficient solutions.
Jonuzaj S, Akula PT, Kleniati PM, et al., 2016, The formulation of optimal mixtures with generalized disjunctive programming: a solvent design case study, AIChE Journal, Vol: 62, Pages: 1616-1633, ISSN: 0001-1541
Systematic approaches for the design of mixtures, based on a computer‐aided mixture/blend design (CAMbD) framework, have the potential to deliver better products and processes. In most existing methodologies the number of mixture ingredients is fixed (usually a binary mixture) and the identity of at least one compound is chosen from a given set of candidate molecules. A novel CAMbD methodology is presented for formulating the general mixture design problem where the number, identity and composition of mixture constituents are optimized simultaneously. To this end, generalized disjunctive programming is integrated into the CAMbD framework to formulate the discrete choices. This generic methodology is applied to a case study to find an optimal solvent mixture that maximizes the solubility of ibuprofen. The best performance in this case study is obtained with a solvent mixture, showing the benefit of using mixtures instead of pure solvents to attain enhanced behavior.
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