Stakeholder-oriented multi-objective process optimization based on an improved genetic algorithm

Improved Harmony Search Algorithm for Enhancing Efficiency and Quality in Optimization of the Distillation Process

Reducing production costs is one of the main objectives of process intensification; in this work, production costs of the distillation process are reduced by reducing equipment size and utility consumption from the perspective of process optimization to achieve the purpose of process intensification. The application of intelligent optimization algorithms in the optimization process of distillation is vital to achieving high efficiency and low costs. Combining the harmony search algorithm with the characteristics of distillation optimization, a new distillation harmony search algorithm (DHSA) was proposed, which includes the self-adaptive adjustment of parameters, roulette selection strategy, and ratio optimization strategy. Benefiting from the DHSA, the optimal total annual cost and calculation times were remarkably reduced when compared with reported algorithms in the optimization of four distillation cases including the two-column model, three-column model, reactive distillation column model, and dividing-wall extractive distillation column model. In addition, the highest coefficient of variation of DHSA in 10 parallel calculations is 1.25%. These results indicate that DHSA has the advantages of a higher-quality solution, less computing time, and higher stability, which not only improves the optimization efficiency and quality but also inspires the optimization strategies for other algorithms.

Zero-Waste Watermelon Production through Nontraditional Rind Flour: Multiobjective Optimization of the Fabrication Process

Watermelon is a fruit produced around the world. Unfortunately, about half of it—the rind—is usually discarded as waste. To transform such waste into a useful product like flour, a thermal treatment is needed. The drying temperature for the rind that produces flour with the best characteristics is most important. A multiobjective optimization (MOO) procedure was applied to define the optimum drying temperature for the rind flour fabrication to be used in bakery products. A neural network model of the fabrication process was developed with the drying temperature as input and five process indicators as outputs. The group of process indicators comprised acidity, pH, water-holding capacity (WHC), oil-holding capacity (OHC), and batch time. Those indicators represent conflicting objectives that are to be balanced by the MOO procedure using the weighted distance method. The MOO process showed that the temperature interval from 67.3 °C to 73.1 °C holds the compromise solutions for the conflicting indicators based on the stakeholder’s preferences. Optimum indicator were 0.12–0.19 g malic acid/100 g dwb (acidity), 5.7–5.8 (pH), 8.93–9.08 g H2O/g dwb (WHC), 1.46–1.56 g oil/g dwb (OHC), and 128–139 min (drying time).

Enhancing the Performance of Evolutionary Algorithm by Differential Evolution for Optimizing Distillation Sequence

Optimal synthesis of distillation sequence is a complex problem in chemical processes engineering, which involves process structure optimization and operation parameters optimization. The study of the synthesis of distillation sequence is a crucial step toward improving the efficiency of chemical processes and reducing greenhouse gas emissions. This work introduced the concept of binary tree to encode the distillation sequence. The performance of the six evolutionary algorithms was evaluated by solving a 14-component distillation sequence synthesis problem. The best algorithm was used to optimize the operation parameters of a triple-column distillation process. The total annual cost and CO₂ emissions were considered as the metrics to evaluate the performance of triple-column distillation processes. As a result, NSGA-II-DE was found to be the best one of the six tested evolutionary algorithms. Then, NSGA-II-DE was applied to the distillation sequence optimization to find the best operating parameters, which led to a significant reduction in CO₂ emission and total annual costs.

Metaheuristics for multiple sequence alignment: A systematic review

The Multiple Sequence Alignment (MSA) is a key task in bioinformatics, because it is used in different important biological analysis, such as function and structure prediction of unknown proteins. There are several approaches to perform MSA and the use of metaheuristics stands out because of the search ability of these methods, which generally leads to good results in a reasonable amount of time. This paper presents a Systematic Literature Review (SLR) on metaheuristics for MSA, compiling relevant works published between 2014 and 2019. The results of our SLR show the constant interest in this subject, due to the several recent publications that use different metaheuristics to obtain more accurate alignments. Moreover, the final results of our SLR show a multi-objective and hybrid approaches trends, which generally leads these methods to achieve even better results. Thus, we show in this work how the use of metaheuristics to perform MSA still remains an important and promising open research field.

Design of international financial risk estimation model based on improved genetic algorithm

The financial industry is developing rapidly, and risk management is an important part of the internal management of financial institutions. In order to accurately estimate international financial risks, improve the risk management performance of financial institutions, and ensure the sustainable development of the international financial market, an international financial risk estimation model based on improved genetic algorithms was designed, the value-at-risk model VAR model was selected to estimate the international financial risk by measuring the degree of economic loss, and the improved genetic algorithm was adopted to the seven parts of immature convergence to quickly obtain the VAR value of international financial risks, including initialize the population, real number coding, determine fitness function, selection operator, crossover operator, mutation operator and predict and process. Results show that the rapid estimation of international financial risks was realized, the designed model can achieve accurate estimation of international financial risks, and the time cost of financial risk estimation under different sample sizes is less than 500 ms.

Zero-Discharge Process for Recycling of Tetrahydrofuran–Water Mixtures

The sustainable design of separation and polymer synthesis processes is of great importance. Therefore, an energy-efficient process for the purification of tetrahydrofuran (THF)–water (H2O) solvent mixtures from an upstream polymer synthesis process in pilot scale was developed with the aim to obtain high purity separation products. The advantages and limitations of a hybrid process in the pilot scale were studied utilizing an Aspen Plus Dynamics® simulation at different pressures to prove the feasibility and energy efficiency. For the rough separation of the two components, distillation was chosen as the first process step. In this way, a separation of a water stream of sufficient quality for further precipitations after polymer synthesis could be achieved. In order to overcome the limitations of the distillation process posed by the azeotropic point of the mixture, a vapor permeation is used, which takes advantage of the heat of evaporation already used in the distillation column. For the purpose of achieving the required low water contents, an adsorption column is installed downstream for final THF purification. This leads to a novel hybrid separation process that is energy efficient and thus allows also the use of the solvents again for upstream polymer synthesis achieving the high purity requirements in a closed-loop process.