Anil Kumar Vinayak, Hridya Ashokan, Sanyukta Sinha, Yogita Halkara, Anand V P Gurumoorthy
{"title":"Role of Biomass Gasification in Achieving Circular Economy","authors":"Anil Kumar Vinayak, Hridya Ashokan, Sanyukta Sinha, Yogita Halkara, Anand V P Gurumoorthy","doi":"10.2174/0124055204319671240515060552","DOIUrl":"https://doi.org/10.2174/0124055204319671240515060552","url":null,"abstract":"\u0000\u0000Growing awareness of environmental concerns and the prioritisation of environmental\u0000stewardship necessitates the incorporation of sustainability practices that are\u0000both economical and profitable. This involves transforming existing industrial practices\u0000from the ‘take-make-waste’ approach to one that aligns with the principles of a circular\u0000economy. This includes the use and restoration of bioreserves or the cycling of products in\u0000a manner that minimizes waste generation by employing the concepts of reuse and recycling.\u0000The adoption of circular economy principles is especially critical in energy-intensive\u0000industries, and there is increased attention to implementing these principles through biomass\u0000gasification. Various methodologies exist for utilizing the potential of biomass by\u0000employing biomass gasification to achieve the desired levels of energy output. Techniques\u0000incorporating circular economy principles for biomass gasification have become increasingly\u0000sought after and achieved widespread implementation in the past few decades. In this\u0000paper, we examine the principle of a circular economy and how biomass gasification can\u0000be leveraged in processes requiring high-energy input to achieve the same.\u0000","PeriodicalId":20833,"journal":{"name":"Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuriy Kochergin, Qing He, T. Hryhorenko, Xiangli Meng
{"title":"Regulation of the Properties of Polymers based on Thiirane using Mixtures of Amine Hardeners","authors":"Yuriy Kochergin, Qing He, T. Hryhorenko, Xiangli Meng","doi":"10.2174/0124055204303723240510115938","DOIUrl":"https://doi.org/10.2174/0124055204303723240510115938","url":null,"abstract":"\u0000\u0000Introduction: The possibility of regulating the curing rate and the complex\u0000of adhesive, deformation-strength and dynamic mechanical properties of polymers\u0000based on bisphenol A dithioester (thiirane) using a mixture of amine hardeners of various\u0000chemical nature is investigated. Method: Diethylenetriamine, diethylenetriaminomethylphenol\u0000and aminopolyamide were investigated as hardeners. The ratio of\u0000the components of the mixed hardener is selected, which provides the best combination\u0000of strength properties. Results: It was found that the rate of adhesion and cohesive\u0000strength at the initial stage (during the first hour) of curing compositions containing a\u0000mixture hardener significantly exceeds compositions cured by individual components\u0000of the mixture. Conclusion: The results of measuring the dynamic mechanical characteristics\u0000of the studied polymers indicate that the dynamic modulus of elasticity,\u0000measured at temperatures below and above the transition from a glassy state to a high\u0000elastic one, for a sample containing a mixed hardener has an intermediate value between\u0000the values characteristic of samples containing individual components of a\u0000mixed hardener.\u0000\u0000\u0000\u0000The ratio of the components of the mixed hardener is selected, which provides the best combination of strength properties.\u0000","PeriodicalId":20833,"journal":{"name":"Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhanhui Wang, Mengzhao Long, Wenlong Duan, Aimin Wang, Xiaojun Li
{"title":"Predicting the Residual Strength of Oil and Gas Pipelines Using the GA-BP Neural Network","authors":"Zhanhui Wang, Mengzhao Long, Wenlong Duan, Aimin Wang, Xiaojun Li","doi":"10.2174/0124055204315589240502052118","DOIUrl":"https://doi.org/10.2174/0124055204315589240502052118","url":null,"abstract":"\u0000\u0000Most NN (neural network) research only conducted qualitative analysis,\u0000analyzing its accuracy, with certain limitations, without studying its NN model, error convergence\u0000process, and pressure ratio. There is relatively limited research on the application of\u0000NN optimized by GA (genetic algorithm) to oil and gas pipelines; Moreover, the residual\u0000strength evaluation of GA-BP NN (genetic algorithm backpropagation neural network) has the\u0000advantages of high global search ability, efficiency not limited by constant differences, and the\u0000use of probability search instead of path search, which has a wide application prospect.\u0000\u0000\u0000\u0000Using MATLAB software, establish GA-BP NN models under five residual strength\u0000evaluation criteria and introduce the relative error of the parameters and the pressure ratio to\u0000comprehensively analyze the accuracy and applicability of GA-BP NN.\u0000\u0000\u0000\u0000Using MATLAB software to estimate the residual strength of oil and gas pipelines with the GA, artificial NN BP, and GA-BP NN.\u0000\u0000\u0000\u0000Firstly, using MATLAB software, a GA-BP NN model was established based on five\u0000residual strength evaluation criteria: ASME B31G Modified, BS7910, PCORRC, DNV RP\u0000F101, and SHELL92, by changing five factors that affect the residual strength of oil and gas\u0000pipelines: diameter, wall thickness, yield strength, corrosion length, and corrosion depth; Second,\u0000the trained GA-BP NN model is used to predict the residual strength of the same set of evaluation\u0000criteria test data and compared with the calculation results of five residual strength evaluation\u0000criteria. The relative error of the parameters and pressure ratio are introduced to comprehensively\u0000analyze the accuracy and applicability of the GA-BP NN.\u0000\u0000\u0000\u0000The error convergence time of the BP NN is longer, and the optimized GA-BP NN has\u0000a shorter convergence time. By comparing the convergence training times of different models, it\u0000can be obtained that for the five sets of residual strength evaluation criteria of ASME B31G\u0000Modified, BS7910, PCORRC, DNV RP F101, and SHELL92, the optimized GA-BP NN model\u0000significantly reduces convergence training times, significantly improves convergence speed, and\u0000further evolves the system performance. From the relative error and local magnification, it can\u0000be seen that for the ASME B31G Modified evaluation criteria, the maximum relative error of\u0000the BP NN model is 1.4008%, and the maximum relative error of the GA-BP NN model is\u00000.7304%. For the evaluation criterion BS7910, the maximum relative error of the BP NN model\u0000is 0.7239%, and the maximum relative error of the GA-BP NN model is 0.5242%; for the evaluation\u0000criteria of DNV RP F101, the maximum relative error of the BP NN model is 1.1260%,\u0000and the maximum relative error of the GA-BP NN model is 0.4810%; for the PCORRC evaluation\u0000criteria, the maximum relative, error and the maximum relative error of the GA-BP NN\u0000model is 0.8004%; for the SHELL92 evaluation criterion, the maximum relative error of the BP\u0000NN model is 1.2292%, and the","PeriodicalId":20833,"journal":{"name":"Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141014766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Thermo-Acoustic Behaviour of K2CrO4 and K4 [Fe(CN)6] in Aqueous\u0000Dimethylformamide at Different Temperatures","authors":"Rajalaxmi Panda, Subhraraj Panda, Susanta Kumar Biswal","doi":"10.2174/0124055204296907240330083154","DOIUrl":"https://doi.org/10.2174/0124055204296907240330083154","url":null,"abstract":"\u0000\u0000Acoustic parameters can help us understand how temperature and\u0000concentration affect the behaviour of potassium ferrocyanide and potassium chromate electrolytes\u0000in the aqueous solvent Dimethylformamide.\u0000\u0000\u0000\u0000The solution's density (ρ), viscosity (η), and ultrasonic speed (u) were measured\u0000at various concentrations and temperatures (ranging from 293 K to 313 K) using a pycnometer,\u0000an Ostwald viscometer, and an ultrasonic interferometer at frequencies of 1MHz,\u0000respectively. Based on these measurements, other acoustic parameters were calculated,\u0000such as free length (Lr), internal pressure (πi), adiabatic compressibility (β), acoustic impedance\u0000(Z), relaxation time (τ), and Gibbs free energy (ΔG).\u0000\u0000\u0000\u0000These acoustic and thermodynamic parameters were used to explore various interactions,\u0000molecular motion, and interaction modes, as well as their effects, which were influenced\u0000by the size of the pure component and the mixtures. The analysis showed that\u0000changes in temperature and concentration led to specific parameter differences, which affected\u0000the interactions between the solute and solvent.\u0000\u0000\u0000\u0000This study demonstrated that increasing the concentration of the mixture increased\u0000the density, viscosity, and ultrasonic velocity due to the interaction between the\u0000solute and solvent, indicating molecular interaction in the mixture.\u0000","PeriodicalId":20833,"journal":{"name":"Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140696974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}