Robust chemical product design via fuzzy optimisation approach

Computer-Aided Framework for the Design of Optimal Bio-Oil/Solvent Blend with Economic Considerations

A major obstacle in utilising pyrolysis bio-oil as biofuel is its relatively low heating value, high viscosity, and non-homogeneity. Solvent addition is a simple yet practical approach in upgrading pyrolysis bio-oil. However, most solvents are often manufactured as specialty chemicals, and thus, this leads to a high production cost of solvents. It is crucial for the designed solvent-oil blend to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. Hence, the objective of this work is to generate feasible solvent candidates by solving this multi-objective optimisation (MOO) problem via a computer-aided molecular design (CAMD) approach. Initially, an optimisation model was developed to identify potential solvents that satisfied the predefined targeted properties. Next, a MOO model was developed via a fuzzy optimisation approach to identify the trade-off between profitability and heating value of the solvent-oil blend. A pricing model was employed to estimate the profitability of the solvent-oil blend. The production of bio-oil in a pyrolysis plant was used to illustrate the applicability of the pricing model. Lastly, phase stability analysis was conducted to ensure the stability and miscibility of the solvent-oil blend. With the developed framework, a promising and cost-effective solvent-oil blend can be generated while displaying optimal biofuel properties.

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics

One of the major drawbacks of cheminformatics is a large amount of information present in the datasets. In the majority of cases, this information contains redundant instances that affect the analysis of similarity measurements with respect to drug design and discovery. Therefore, using classical methods such as the protein bank database and quantum mechanical calculations are insufficient owing to the dimensionality of search spaces. In this paper, we introduce a hybrid metaheuristic algorithm called CHHO-CS, which combines Harris hawks optimizer (HHO) with two operators: cuckoo search (CS) and chaotic maps. The role of CS is to control the main position vectors of the HHO algorithm to maintain the balance between exploitation and exploration phases, while the chaotic maps are used to update the control energy parameters to avoid falling into local optimum and premature convergence. Feature selection (FS) is a tool that permits to reduce the dimensionality of the dataset by removing redundant and non desired information, then FS is very helpful in cheminformatics. FS methods employ a classifier that permits to identify the best subset of features. The support vector machines (SVMs) are then used by the proposed CHHO-CS as an objective function for the classification process in FS. The CHHO-CS-SVM is tested in the selection of appropriate chemical descriptors and compound activities. Various datasets are used to validate the efficiency of the proposed CHHO-CS-SVM approach including ten from the UCI machine learning repository. Additionally, two chemical datasets (i.e., quantitative structure-activity relation biodegradation and monoamine oxidase) were utilized for selecting the most significant chemical descriptors and chemical compounds activities. The extensive experimental and statistical analyses exhibit that the suggested CHHO-CS method accomplished much-preferred trade-off solutions over the competitor algorithms including the HHO, CS, particle swarm optimization, moth-flame optimization, grey wolf optimizer, Salp swarm algorithm, and sine-cosine algorithm surfaced in the literature. The experimental results proved that the complexity associated with cheminformatics can be handled using chaotic maps and hybridizing the meta-heuristic methods.