• Energy Research
• 2021-2025close
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Deep eutectic solvents (DESs) are an alternative to traditional organic solvents and ionic liquids and meet the requirements of "green" chemistry. They are easy to prepare using low-cost constituents, are non-toxic and biodegradable. The review analyzes literature on the use of DES in various fields of biotechnology, provides data on the types of DESs, methods for their preparation, and properties. The main areas of using DESs in biotechnology include extraction of physiologically active substances from natural resources, pretreatment of lignocellulosic biomass to improve enzymatic hydrolysis of cellulose, production of bioplastics, as well as a reaction medium for biocatalytic reactions. The aim of this review is to summarize available information on the use of new solvents for biotechnological purposes.

AbstractNowadays, researchers are focusing on the methods of using recycled industrial/agricultural wastes as raw materials, which are not only economically but also environmentally useful. In the recent years, many scholars have conducted research on the rational use of rice husks (RHs) and found that RHs are rich in inorganic/organic components and used in combination with the certain building materials to produce new green composite building materials. This paper studies the method of using RH processed products and the sodium silicate solution (liquid glass) made from RH ash to produce cheap wood substitute composite materials, which can be improved the insulation performance of the wall greatly. Obtained results are determined by comparing with the properties of standard polymer composite materials such as particleboards, chipboards, and medium‐density fiberboards (MDF). The significance of the work is pointing out by solving the problem of recycling large‐tonnage agricultural waste and reproduce products with the consumer's value.

Abstract This study presents and evaluates the feasibility of a novel hybridization of modified Kalina cycle, reverse osmosis desalination, and low-temperature water electrolysis utilizing geothermal energy to yield power, distilled water, and hydrogen, respectively. The scientific impact of the current work has been improved considering the features of Sabalan flash-binary geothermal wells in Iran as a real model through a case study. In addition to designing a novel setup, the smart use of multi-heat recovery technique, modifying the base cycle, and utilizing a part of generated distilled water to produce hydrogen by the electrolyzer are the other structural originalities, distinguishing the current work from the previous studies. The suggested system is scrutinized via a parametric study and optimized based on a genetic algorithm. The parametric study demonstrated that the highest sensitivity of varying the performance criteria of the whole system is attributed to the change in flash tank pressure. Moreover, the multi-objective optimization led to achieving the exergy efficiency and trigeneration gain output ratio as 51.3% and 1.7 for the system, respectively. Furthermore, the system was able to produce 4795 kW of power, 5.3 kg/h of hydrogen, and 19.9 kg/s of distilled water.

The article describes the results of work on the creation of neural networks calculating the technical and economic parameters of all possible modes of operation of a thermal turbine unit with a steam turbine of type PT-60-130/13. In accordance with the objectives set, recommendations for the preparation of training data samples are formulated. The input and output parameters of the condensing and heating modes of operation of the steam turbine are determined. The results of research on determining the most optimal architecture of neural networks for calculating the energy characteristics of steam turbine plants of the heating type are presented. The results of calculations of the mean squared errors of neural network predictions from the results of calculations performed using a verified object-oriented model of a PT-60-130/13 turbine unit are tabulated.Graphs of the dependence of the specific heat consumption for the generation of electrical energy on the power of a PT-60-130/13 turbine unit for condensation and heating modes of operation using neural networks are plotted. The conclusion is formulated about the possibility of using neural networks for the development of energy characteristics and regulatory documentation on fuel use of equipment of thermal power plants.