Pub Date : 2024-05-20DOI: 10.2174/0124055204319671240515060552
Anil Kumar Vinayak, Hridya Ashokan, Sanyukta Sinha, Yogita Halkara, Anand V P Gurumoorthy
Growing awareness of environmental concerns and the prioritisation of environmental stewardship necessitates the incorporation of sustainability practices that are both economical and profitable. This involves transforming existing industrial practices from the ‘take-make-waste’ approach to one that aligns with the principles of a circular economy. This includes the use and restoration of bioreserves or the cycling of products in a manner that minimizes waste generation by employing the concepts of reuse and recycling. The adoption of circular economy principles is especially critical in energy-intensive industries, and there is increased attention to implementing these principles through biomass gasification. Various methodologies exist for utilizing the potential of biomass by employing biomass gasification to achieve the desired levels of energy output. Techniques incorporating circular economy principles for biomass gasification have become increasingly sought after and achieved widespread implementation in the past few decades. In this paper, we examine the principle of a circular economy and how biomass gasification can be leveraged in processes requiring high-energy input to achieve the same.
{"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":"89 19","pages":""},"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}
Pub Date : 2024-05-17DOI: 10.2174/0124055204303723240510115938
Yuriy Kochergin, Qing He, T. Hryhorenko, Xiangli Meng
Introduction: The possibility of regulating the curing rate and the complex of adhesive, deformation-strength and dynamic mechanical properties of polymers based on bisphenol A dithioester (thiirane) using a mixture of amine hardeners of various chemical nature is investigated. Method: Diethylenetriamine, diethylenetriaminomethylphenol and aminopolyamide were investigated as hardeners. The ratio of the components of the mixed hardener is selected, which provides the best combination of strength properties. Results: It was found that the rate of adhesion and cohesive strength at the initial stage (during the first hour) of curing compositions containing a mixture hardener significantly exceeds compositions cured by individual components of the mixture. Conclusion: The results of measuring the dynamic mechanical characteristics of the studied polymers indicate that the dynamic modulus of elasticity, measured at temperatures below and above the transition from a glassy state to a high elastic one, for a sample containing a mixed hardener has an intermediate value between the values characteristic of samples containing individual components of a mixed hardener. The ratio of the components of the mixed hardener is selected, which provides the best combination of strength properties.
简介研究了使用不同化学性质的胺类固化剂混合物调节双酚 A 二硫酯(硫烷)聚合物的固化速率以及粘合力、变形强度和动态机械性能的可能性。方法:研究了作为固化剂的二乙烯三胺、二乙烯三胺甲基苯酚和氨基多酰胺。选择混合固化剂中各组分的比例,以获得最佳的强度性能组合。研究结果结果发现,含有混合固化剂的固化组合物在初始阶段(第一小时)的粘附率和内聚强度明显高于由混合物的单个成分固化的组合物。结论对所研究聚合物的动态机械特性进行测量的结果表明,在低于和高于从玻璃态向高弹性态过渡的温度下测量的含有混合固化剂的样品的动态弹性模量,其数值介于含有混合固化剂单个成分的样品的数值之间。
{"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":" 2","pages":""},"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}
Pub Date : 2024-05-03DOI: 10.2174/0124055204315589240502052118
Zhanhui Wang, Mengzhao Long, Wenlong Duan, Aimin Wang, Xiaojun Li
Most NN (neural network) research only conducted qualitative analysis, analyzing its accuracy, with certain limitations, without studying its NN model, error convergence process, and pressure ratio. There is relatively limited research on the application of NN optimized by GA (genetic algorithm) to oil and gas pipelines; Moreover, the residual strength evaluation of GA-BP NN (genetic algorithm backpropagation neural network) has the advantages of high global search ability, efficiency not limited by constant differences, and the use of probability search instead of path search, which has a wide application prospect. Using MATLAB software, establish GA-BP NN models under five residual strength evaluation criteria and introduce the relative error of the parameters and the pressure ratio to comprehensively analyze the accuracy and applicability of GA-BP NN. Using MATLAB software to estimate the residual strength of oil and gas pipelines with the GA, artificial NN BP, and GA-BP NN. Firstly, using MATLAB software, a GA-BP NN model was established based on five residual strength evaluation criteria: ASME B31G Modified, BS7910, PCORRC, DNV RP F101, and SHELL92, by changing five factors that affect the residual strength of oil and gas pipelines: diameter, wall thickness, yield strength, corrosion length, and corrosion depth; Second, the trained GA-BP NN model is used to predict the residual strength of the same set of evaluation criteria test data and compared with the calculation results of five residual strength evaluation criteria. The relative error of the parameters and pressure ratio are introduced to comprehensively analyze the accuracy and applicability of the GA-BP NN. The error convergence time of the BP NN is longer, and the optimized GA-BP NN has a shorter convergence time. By comparing the convergence training times of different models, it can be obtained that for the five sets of residual strength evaluation criteria of ASME B31G Modified, BS7910, PCORRC, DNV RP F101, and SHELL92, the optimized GA-BP NN model significantly reduces convergence training times, significantly improves convergence speed, and further evolves the system performance. From the relative error and local magnification, it can be seen that for the ASME B31G Modified evaluation criteria, the maximum relative error of the BP NN model is 1.4008%, and the maximum relative error of the GA-BP NN model is 0.7304%. For the evaluation criterion BS7910, the maximum relative error of the BP NN model is 0.7239%, and the maximum relative error of the GA-BP NN model is 0.5242%; for the evaluation criteria of DNV RP F101, the maximum relative error of the BP NN model is 1.1260%, and the maximum relative error of the GA-BP NN model is 0.4810%; for the PCORRC evaluation criteria, the maximum relative, error and the maximum relative error of the GA-BP NN model is 0.8004%; for the SHELL92 evaluation criterion, the maximum relative error of the BP NN model is 1.2292%, and the
大多数 NN(神经网络)研究只是进行定性分析,分析其精度,具有一定的局限性,没有对其 NN 模型、误差收敛过程和压力比进行研究。此外,GA-BP 神经网络(遗传算法反向传播神经网络)的剩余强度评价具有全局搜索能力强、效率不受常量差异限制、用概率搜索代替路径搜索等优点,具有广泛的应用前景。利用 MATLAB 软件,在五种残余强度评价准则下建立 GA-BP NN 模型,并引入参数相对误差和压力比,全面分析 GA-BP NN 的准确性和适用性。首先,利用 MATLAB 软件,基于五种残余强度评价准则建立了 GA-BP NN 模型:首先,利用 MATLAB 软件,基于 ASME B31G Modified、BS7910、PCORRC、DNV RPF101 和 SHELL92 五种残余强度评价标准,通过改变直径、壁厚、屈服强度、腐蚀长度和腐蚀深度五个影响因素,建立了 GA-BP NN 模型;其次,利用训练好的 GA-BP NN 模型预测同一组评价标准试验数据的残余强度,并与五种残余强度评价标准的计算结果进行比较。引入参数相对误差和压力比来综合分析 GA-BP NN 的准确性和适用性。通过比较不同模型的收敛训练时间,可以得出对于 ASME B31GModified、BS7910、PCORRC、DNV RP F101 和 SHELL92 五套残余强度评价标准,优化后的 GA-BP NN 模型显著减少了收敛训练时间,明显提高了收敛速度,系统性能得到进一步提高。从相对误差和局部放大率可以看出,对于 ASME B31G Modified 评价标准,BP NN 模型的最大相对误差为 1.4008%,而 GA-BP NN 模型的最大相对误差为 0.7304%。在 BS7910 评估标准中,BP NN 模型的最大相对误差为 0.7239%,GA-BP NN 模型的最大相对误差为 0.5242%;在 DNV RP F101 评估标准中,BP NN 模型的最大相对误差为 1.1260%,GA-BP NN 模型的最大相对误差为 0.4810%;在 PCORRC 评估标准中,GA-BP NN 模型的最大相对误差和最大相对误差均为 0.8004%;在 SHELL 评估标准中,BP NN 模型的最大相对误差为 0.7239%,GA-BP NN 模型的最大相对误差为 0.5242%。GA-BP NN的预测结果与五种剩余强度评价标准的计算结果较为接近,预测效果较好,可以较为准确地预测油气管道的剩余强度。根据压力比,BP NN 模型在五种残余强度标准下的平均压力比 A 为 1.0004,GA-BP NN 模型的平均压力比 A 为 0.9998。这些发现对腐蚀性油气管道残余强度的预测具有重要意义。
{"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":"66 2","pages":""},"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}
Acoustic parameters can help us understand how temperature and concentration affect the behaviour of potassium ferrocyanide and potassium chromate electrolytes in the aqueous solvent Dimethylformamide. The solution's density (ρ), viscosity (η), and ultrasonic speed (u) were measured at various concentrations and temperatures (ranging from 293 K to 313 K) using a pycnometer, an Ostwald viscometer, and an ultrasonic interferometer at frequencies of 1MHz, respectively. Based on these measurements, other acoustic parameters were calculated, such as free length (Lr), internal pressure (πi), adiabatic compressibility (β), acoustic impedance (Z), relaxation time (τ), and Gibbs free energy (ΔG). These acoustic and thermodynamic parameters were used to explore various interactions, molecular motion, and interaction modes, as well as their effects, which were influenced by the size of the pure component and the mixtures. The analysis showed that changes in temperature and concentration led to specific parameter differences, which affected the interactions between the solute and solvent. This study demonstrated that increasing the concentration of the mixture increased the density, viscosity, and ultrasonic velocity due to the interaction between the solute and solvent, indicating molecular interaction in the mixture.
{"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":"8 14","pages":""},"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}