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The SAFT‐ɣ Mie group‐contribution equation of state is used to represent the fluid‐phase behaviour of aqueous solutions of a variety of linear, branched, and cyclic amines. New group interactions are developed in order to model the mixtures of interest, including the like and unlike interactions between alkyl primary, secondary, and tertiary amine groups (NH2, NH, N), cyclic secondary and tertiary amine groups (cNH, cN), and cyclohexylamine groups (cCHNH, cCHN) with water (H2O). The group‐interaction parameters are estimated from appropriate experimental thermodynamic data for pure amines and selected mixtures. By taking advantage of the group‐contribution nature of the method, one can describe the fluid‐phase behaviour of mixtures of molecules comprising those groups over broad ranges of temperature, pressure, and composition. A number of aqueous solutions of amines are studied including linear, branched aliphatic, and cyclic amines. Liquid‐liquid equilibria (LLE) bounded by lower critical solution temperatures (LCSTs) have been reported experimentally and are reproduced here with SAFT‐ɣ Mie approach. The main feature of the approach is the ability not only to represent accurately the experimental data employed in the parameter estimation, but also to predict the vapour‐liquid, liquid‐liquid, and vapor‐liquid‐liquid equilibria, and LCSTs with the same set of parameters. Pure compound and binary phase diagrams of diverse types of amines and their aqueous solutions are assessed in order to demonstrate the main features of the thermodynamic and fluid‐phase behaviour.

Molecular-level decisions are increasingly recognized as an integral part of process design. Finding the optimal process performance requires the integrated optimization of process and solvent chemical structure, leading to a challenging mixed-integer nonlinear programming (MINLP) problem. The formulation of such problems when using a group contribution version of the statistical associating fluid theory, SAFT-γ Mie, to predict the physical properties of the relevant mixtures reliably over process conditions is presented. To solve the challenging MINLP, a novel hierarchical methodology for integrated process and solvent design (hierarchical optimization) is presented. Reduced models of the process units are developed and used to generate a set of initial guesses for the MINLP solution. The methodology is applied to the design of a physical absorption process to separate carbon dioxide from methane, using a broad selection of ethers as the molecular design space. The solvents with best process performance are found to be poly(oxymethylene)dimethylethers. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3249–3269, 2015

A hybrid experimental/computer-aided methodology for the design of solvents for reactions is presented. It is based on the use of a few reaction rate measurements to build a reaction model, followed by the formulation and solution of an optimal computer-aided molecular design problem (CAMD) in which the reaction rate under given conditions is maximized. In order to verify the suitability of the solvent candidates identified in the CAMD step, and to assess the reliability of the model used, feedback can be introduced. When the reliability of the model is found to be insufficient, experimental rate data for the candidate solvents are obtained and added to the original data set to create an updated reaction model, which can be used to find new candidate solvents. Since very few measurements are used to build the reaction model, we perform a sensitivity analysis on the model to assess the impact of uncertainty. Using this information to generate scenarios, we then solve a stochastic optimization problem, which aims to determine the solvents that give the best average performance. The final output consists of a list of candidate solvents which can be targeted for experimentation. This methodology is illustrated, step by step, through application to a solvolysis reaction. © 2007 American Institute of Chemical Engineers AIChE J, 2007

A mixed-integer dynamic optimization (MIDO) framework for solvent design in batch processes is presented. Performance measures reflecting process economics and computed on the basis of process dynamics are used to validate candidate solvent structures built from the UNIFAC molecular groups. These define the discrete space in the overall material formulation and are combined according to molecular design feasibility rules to guarantee realistic molecular representations. The algorithm is based on the decomposition of the MIDO primal subproblem into several steps that are solved successively. This allows unsuitable solvents to be detected and discarded quickly and without significant computational cost. Emphasis is placed on problem formulation in order to match accuracy of process model and physical property predictions. The algorithm is applied successfully to an industrial case study dealing with a three-phase dehydration column and a decantation unit for solvent recovery. The proposed algorithm can be regarded as a promising initial step toward an integrated and simultaneous methodology for material process design in batch separation systems.

The effectiveness of any methodology used to identify hazards in chemical processes affects both safety and economics. To achieve maximum safety at minimum cost, a conservative, but realistic, analysis must be carried out. An approach to hazard identification is proposed based on a detailed process model which includes nonlinear dynamics and uncertainty. A new modeling framework, the region-transition model (RTM), is developed, which enables the simulation of regions of the operating space through an extension of the hybrid state transition system formalism. The RTM is illustrated on a nonlinear batch reactor with parameter uncertainty. A safety-verification algorithm identifies regions of the input space (initial conditions and external inputs) which guarantee safe operation. The algorithm is successfully applied to three examples: a tank with overflow and underflow, a batch reactor with an exothermic reaction, and a CSTR with feed preheating.

Two novel deterministic global optimization algorithms for nonconvex mixed-integer problems (MINLPs) are proposed, using the advances of the αBB algorithm for nonconvex NLPs of Adjiman et al. The special structure mixed-integer αBB algorithm (SMIN-αBB) addresses problems with nonconvexities in the continuous variables and linear and mixed-bilinear participation of the binary variables. The general structure mixed-integer αBB algorithm (GMIN-αBB) is applicable to a very general class of problems for which the continuous relaxation is twice continuously differentiable. Both algorithms are developed using the concepts of branch-and-bound, but they differ in their approach to each of the required steps. The SMIN-αBB algorithm is based on the convex underestimation of the continuous functions, while the GMIN-αBB algorithm is centered around the convex relaxation of the entire problem. Both algorithms rely on optimization or interval-based variable-bound updates to enhance efficiency. A series of medium-size engineering applications demonstrates the performance of the algorithms. Finally, a comparison of the two algorithms on the same problems highlights the value of algorithms that can handle binary or integer variables without reformulation.