Berhane H. Gebreslassie

Efficient ant colony optimization for computer aided molecular design: Case study solvent selection problem

Abstract In this paper, we propose a novel computer-aided molecular design (CAMD) methodology for the design of optimal solvents based on an efficient ant colony optimization (EACO) algorithm. The molecular design problem is formulated as a mixed integer nonlinear programming (MINLP) model in which a solvent performance measure is maximized (solute distribution coefficient) subject to structural feasibility, property, and process constraints. In developing the EACO algorithm, the better uniformity property of Hammersley sequence sampling (HSS) is exploited. The capabilities of the proposed methodology are illustrated using a real world case study for the design of an optimal solvent for extraction of acetic acid from waste process stream using liquid–liquid extraction. The UNIFAC model based on the infinite dilution activity coefficient is used to estimate the mixture properties. New solvents with better targeted properties are proposed.

Efficient Ant Colony Optimization (EACO) Algorithm for DeterministicOptimization

In this paper, an efficient ant colony optimization (EACO) algorithm is proposed based on efficient sampling method for solving combinatorial, continuous and mixed-variable optimization problems. In EACO algorithm, Hammersley Sequence Sampling (HSS) is introduced to initialize the solution archive and to generate multidimensional random numbers. The capabilities of the proposed algorithm are illustrated through 9 benchmark problems. The results of the benchmark problems from EACO algorithm and the conventional ACO algorithm are compared. More than 99% of the results from the EACO show efficiency improvement and the computational efficiency improvement range from 3% to 71%. Thus, this new algorithm can be a useful tool for large-scale and wide range of optimization problems. Moreover, the performance of the EACO is also tested using the five variants of ant algorithms for combinatorial problems.

Multi-objective optimization of solar assisted absorption cooling system

This work presents a multi-period and multi-objective optimization based on mathematical programming of solar assisted absorption cooling systems. Seven solar collector models combined with a gas fired heater and an absorption cooling cycle are considered. The optimization task is formulated as a multi-objective multi-period mixed-integer nonlinear programming (MINLP) problem that accounts for the minimization of the total cost of the cooling system and the associated environmental impact. The environmental performance is measured following the Life Cycle Assessment (LCA) principles. The capabilities of the proposed method are illustrated in a case study that addresses the design of a solar assisted ammonia-water absorption cooling system using the weather conditions of Tarragona (Spain).

Design of novel polymeric adsorbents for metal ion removal from water using computer-aided molecular design

Heavy metals in drinking water act as contaminants that can cause serious health problems. These metal ions in drinking water are generally removed using cation exchange resins that are used as adsorbents. Generally, chelating resins with limited adsorption capacity are commercially available. Manufacturing novel resin polymers with enhanced adsorption capacity of metal ion requires ample experimental efforts that are expensive as well as time consuming. To overcome these difficulties, application of computer-aided molecular design (CAMD) will be an efficient way to develop novel chelating resin polymers. In this paper, CAMD based on group contribution method (GCM) has been used to design novel resins with enhanced adsorption capability of removing heavy metal ions from water. A polymer consists of multiple monomer units that repeat in a polymer chain. Each repeat unit of the polymer can be subdivided into different structural and functional groups. The adsorption mechanism of heavy metals on resin depends on the difference between activities in adsorbents and the bulk fluid phase. The contribution of the functional groups in the adsorption process is found by estimating the activity coefficient of heavy metal in the solid phase and bulk phase using a modified version of the UNIFAC GCM. The interaction parameters of the functional groups are first determined and then they are used in a combinatorial optimization method for CAMD of novel resin polymers. In this work, designs of novel resin polymers for the removal of Cu ions from drinking water are used as a case study. The proposed new polymer resin has an order of magnitude higher adsorption capacity compared to conventional resin used for the same purpose.

Efficient ant colony optimization (EACO) for solvent selection using computer aided molecular design

Abstract Efficient ant colony optimization (EACO) is a new metaheuristic optimization algorithm for tackling linear, nonlinear and mixed integer nonlinear(MINLP) programming problems. In this work, a solvent selection optimization problem modeled based on a novel computer-aided molecular design (CAMD) methodology is optimized using an EACO algorithm. The molecular design problem is formulated as an MINLP model where a solvent solute distribution coefficient is maximized subject to structural feasibility, property and process constraints. The capability of the proposed methodology is illustrated through a case study of an extraction of acetic acid from waste process stream by liquid-liquid extraction. Environmentally benign new solvents with better targeted properties are proposed.

Heterogeneous multi-agent optimization framework with application to synthesizing optimal nuclear waste blends

Multi-agent optimization method is a nature-inspired framework that supports the cooperative search of an optimal solution of an optimization problem by a group of algorithmic agents connected through an environment with certain predefined information sharing protocol. In this work, we propose a novel heterogeneous multi-agent optimization (HTMAO) framework. The proposed framework is validated using a set of benchmark problems a real-world synthesizing radioactive waste blending problem. The optimal radioactive waste blending problem is formulated as a mixed integer nonlinear programming. The total frit used for vitrification process is minimized subject to thermodynamic properties and process model constraints. The model simultaneously determines the optimal decisions that include the combination of the waste tanks that form each waste blend and the amount of frit needed for the vitrification of each waste blend. In developing the HTMAO framework, efficient ant colony optimization algorithms; efficient simulated annealing; efficient genetic algorithm; and sequential quadratic programming solver are considered as algorithmic agents. We illustrate this approach through a real-world case study of the optimal radioactive waste blending of Hanford site in Southern Washington where nuclear waste is stored.