Separación, identificación y cuantificación

Process design and molecular design have been treated as two separate problems generally, with little to no feedback between the two problems. Approaches developed to solve individual process design and molecular design problems show limitations, due to lack of information required prior to start the design algorithm. For example, in molecular design techniques, input of desired target properties is always required to start the design procedure, which is assumed ahead of the design and usually based on previous experience or knowledge. This situation can lead to a sub-optimal design. Similarly, a list of pre-defined candidate components are given generally when considering conventional process design methodologies, and hence limits the performance of process to the listed components. These limitations can be overcome through considering the interactions between both problems as shown in Figure 2-4 (Eden et al., 2004). The necessary input to this methodology is the molecular building blocks to form the desired product for molecular design problem and the desired process performance for process

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design problem. The outputs of this methodology are design variables that fulfil the desired process performance target, and molecules that satisfy the property targets identified from solution of the process design problem. The interconnections between molecular design and process design enable exchange of information; hence avoid pre-deciding of any specific compounds and assumption on target property values.

Discrete decisions

(e.g. type of compound, number of functional groups)

Continuous decisions

(e.g. operating conditions)

Molecular design Given set of molecular groups to be screened (building blocks) Discrete decisions

(e.g. structural modifications)

Continuous decisions

(e.g. operating conditions)

Process design

Desired process performance (e.g. recovery, yield,

cost)

Candidate molecules (e.g. raw materials, MSA’s)

Constraints on property values obtained by targeting

optimum process performance

Figure 2-4: Integrated process-product design

Various works have been done on integrating process and product design problems to solve both problems sequentially or simultaneously. Lee et

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al. (2002) proposed a selection method combining thermodynamics and non- linear programming techniques for optimal refrigerant mixture compositions, and developed a systematic design tool for mixed refrigerant systems. Eden et al. (2004) introduced a systematic framework for simultaneous solution of process and product design problems based on property clustering technique. The proposed methodology reformulates the conventional forward problems into two reverse problem formulations: reverse of simulation problem and reverse of property prediction problem. Papadopoulos and Linke (2005) presented a unified framework for integrated solvent design and process system design, which allows the identification of solvent molecules, based on process performance criteria. Papadopoulos and Linke (2006) later proposed to incorporate the solvent design information into process synthesis stage through molecular clustering approach, within an integrated design of solvent and process system. Optimal solvent candidates are identified using multi- objective optimisation, and subsequently evaluated in a process synthesis stage.

Eljack et al. (2007) proposed a systematic property based framework for simultaneous process and molecular design. Using this approach, process design problem is solved in terms of properties and property targets corresponding to the desired process performance is obtained. Potential molecular will be determined to match the obtained targets. For a process and product design problem that can be described by three properties, it can be

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solved visually and simultaneously using this framework. Kazantzi et al. (2007) presented a graphical approach for simultaneous process and molecular design, and derived design rules for this purpose. In this proposed approach, process requirements and objectives, as well as molecular group properties were integrated to simultaneously target process design and material design. Property clustering technique was combined with GC methods to map the system from process level to molecule level using this approach, and vice versa. Therefore, the process design problem can be reversely mapped to define the constraints for molecular design problem. Nonetheless, mathematical optimisation based approaches can easily extend the application range to include more properties (Eljack et al., 2008).

Chemmangattuvalappil et al. (2010b) developed a combined property clustering and GC+ algorithm to identify molecules that meet the property targets identified during the process design stage. This approach is useful when property contributions of some molecular groups are not available. To design simple monofunctional molecules, a modified visual approach was proposed; while mathematical optimisation method was developed to design more complicated structures. Besides, higher order groups can be considered to increase the accuracy of prediction when mathematical approach is used. Bardow et al. (2010) presented continuous-molecular targeting method (CoMT-CAMD) to solve integrated process and product design problem. An optimal process and a hypothetical solvent are first obtained using CoMT-

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CAMD, and the parameters of hypothetical solvent are mapped onto an existing optimal solvent afterwards.

Bommareddy et al. (2012) proposed a hybrid method combining computer-aided flowsheet design (CAFD) and CAMD, based on GC approaches. Therefore, evaluation of the solution alternatives for both problems is straightforward and rapid. Ng et al. (2015) presented a two-stage optimisation approach to design optimal biochemical products and synthesise optimal conversion pathways in a biorefinery. The optimal biochemical products that meet the customer requirements are first identified using signature based molecular design techniques, followed by determining optimal conversion pathways to convert biomass into the identified products through superstructural mathematical optimisation approach. Ng et al. (2015b) later extended the approach to integrate mixture design and process design for biorefinery.