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ML based catalyst modelling for photo-catalytic reduction of CO2 to methanol

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ML based catalyst modelling for photo-catalytic reduction of...

作者： Voleti, Lakshmi Devi Kamesh, Reddi Rani, K Yamuna Process Engineering and Technology Transfer Department CSIR – Indian Institute of Chemical Technology Hyderabad500007 India

关键词： Carbon dioxide

Artificial Neural Network Modelling of Reverse Electrodialysis 2nd

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Artificial Neural Network Modelling of Reverse Electrodialys...

2nd International Conference on Water Technologies, ICWT 2022

作者： Sen, Shruti Rath, Rudra Kamesh, Reddi Roy, Anirban Department of Chemical Engineering Goa BITS Pilani Goa Pilani403726 India Process Engineering and Technology Transfer Department CSIR-Indian Institute of Chemical Technology Hyderabad500007 India

ISBN: (纸本)9789819967612

Reverse electrodialysis (RED) is an emerging technology with the potential to generate energy from salinity power gradients with limited environmental pollution. In a salinity-gradient power generator (SGP-RED), cation and anion exchange membranes are stacked alternately and due to the difference in concentrations of river and seawater, the diffusion of ions generates an electrochemical potential. In this work, a data-driven approach has been adopted for predicting the performance (Power Density) of SGP-RED power generators over different operating conditions and membrane types, using Artificial Neural Network (ANN) models. Experimental data was gathered and mined from 130 research publications from the last ten years to predict the output Power density of the system against a set of nine input parameters, e.g. membrane types, thickness, resistance, current density, perm selectivity, temperature, etc. A simple mathematical model to estimate the Power density is proposed and used to validate the experimental data before using them with ANN. We used ANN models using Bayesian Regularisation as the backpropagation algorithm (ANN-BR) along with a combination of six sets of pair-wise activation functions amongst Tan-sigmoid, Log-Sigmoid and Linear, in hidden and output layers. The ANN-BR model with Tan-Sigmoid activation functions in both hidden and output layers is shown to predict most accurately the Power density of the SGP-RED system. © 2023, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： Neural networks

Sustainable Approach to Biodiesel Production Using Hydrodynamic Cavitation Route

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Sustainable Approach to Biodiesel Production Using Hydrodyna...

International Conference on Chemical, Bio and Environmental engineering, CHEMBIOEN 2021

作者： Mishra, Birupakshya Thakare, Atharv Mukherjee, Anupam Mullick, Aditi Moulik, Siddhartha Roy, Anirban Cavitation and Dynamics Lab Department of Process Engineering and Technology Transfer CSIR-Indian Institute of Chemical Technology Hyderabad500007 India Water-Energy Nexus Lab Department of Chemical Engineering BITS Pilani Goa Campus Goa403726 India

ISBN: (纸本)9783030965532

Complete reliance on fossil fuels has to lead to skyrocketing prices with detrimental environmental conditions. This drove the search for an alternative source of energy with low emissions and higher energy. In this regard, biodiesel has gained importance over the last few years as a clean, sustainable, and renewable energy resource. However, for producing biodiesel commercially it is important to recognize technologies that are sustainable, energetically feasible, and technologically viable. In this context, Hydrodynamic cavitation provides ample scope for the synthesis of biodiesel in the simplest, energy-efficient, and environmentally friendly with high yield. In, the present work, a detailed analysis of intensified biodiesel production using HC is explained. The present chapter begins with a discussion on the acquisition of feedstock and the role feedstock played in the characterization of biodiesel. Furthermore, it also highlights the reaction mechanism for transesterification reaction employed chiefly for the production of biodiesel. The chapter further delves into biodiesel production using different reactors and their technological advancements. The chapter then proceeds to discuss the state of art HC technology and its use in biodiesel production. The chapter ends with the recent trends and future perspectives of biodiesel production concluding biodiesel prepared by this technology is scalable, energy-efficient, and eco-friendly. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biodiesel

Profenofos Insecticide Degradation by Hydrodynamic Cavitation and Other Aops: An Insight into Ozone Pretreatment for Synergy

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SSRN 2022年

作者： Vadthya, Pavani Process Engineering and Technology Transfer Department CSIR-Indian Institute of Chemical Technology Telangana Hyderabad500007 India

The physico-chemical degradation of Profenofos, an organo-phosphorous insecticide in aqueous streams was studied by hydrodynamic cavitation (HC) and its integrated advanced oxidation processes (AOPs). Profenofos being a hazardous emerging micro pollutant, development of a sustainable process for its complete degradation with feasible techno economics is focused in the investigation. In general, the effective combinations of AOPs for safer disposal of water is also targeted in the manuscript. In this study, initially Profenofos degradation is studied by stand alone HC, ozonation and H 2 O 2 treatment techniques. HC being an emerging green route for organic degradation, studies have been conducted to optimize the process parameters and also to identify the best AOP integration strategy for establishment of techno-economic feasibility of the process. Based on the techno-economics, kinetics and byproduct analysis the case study ‘for the first time’ reveals that conducting ozonation process as a pretreatment to HC yields higher synergistic effect and rapid degradation of Profenofos as compared to simultaneous integration of HC with ozonation or H 2 O 2 treatment. Standalone HC process is found to be most cost effective process as compared to other AOPs and integrated processes. The degradation kinetics and byproduct analysis are also detailed and discussed in the study for insightful observations. © 2022, The Authors. All rights reserved.

关键词： Degradation

Strong Interaction Heterointerface of Nife Oxyhydroxide/Cerium Oxide for Efficient and Stable Water Oxidation

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SSRN 2024年

作者： Jiao, Han Wang, Chun Tian, Hao Zhang, Zi-Yang Zhao, Yang Na, Ping Yamauchi, Yusuke Wang, Zhong-Li Tianjin Laboratory of Mass Transfer & Separation Process Tianjin Key Laboratory of Applied Catalysis Science & Technology School of Chemical Engineering and Technology Tianjin University Tianjin300354 China Research Institute of Petroleum Processing SINOPEC Beijing100083 China The University of Queensland BrisbaneQLD4072 Australia Department of Materials Process Engineering Graduate School of Engineering Nagoya University Nagoya464−8603 Japan

NiFe oxyhydroxides (NiFeOxHy) are the benchmark catalysts for alkaline oxygen evolution reaction (OER), however, it is still challenging for these electrocatalysts to achieve both high activity and long-term stability. Herein, we demonstrate that strong interaction heterointerface of NiFeOxHy/cerium oxide (CeO2) can control the reconstruction process of NiFeOxHy and the reaction pathway of OER, which simultaneously improves the activity and stability of the catalyst. Interestingly, after electrochemical activation, with and without CeO2, NiFeOxHy oxyhydroxides exhibit completely different structures, with the former being uniformly distributed ultrafine nanosheets and the latter being spherical agglomerates of larger sized nanosheets. The strong heterointerface interaction induces the electrons of Ni and Fe to shift to Ce. The optimized catalyst exhibits a low overpotential of 240 mV at 100 mA cm-2 with the durability over 2000 h at 200 mA cm-2. The OER mechanism studies indicate that NiFeOxHy/CeO2 heterointerface follows lattice oxygen oxidation mechanism with accelerated kinetics. © 2024, The Authors. All rights reserved.

关键词： Cerium oxide

Machine Learning Based Catalyst Modelling for Direct Conversion of Co2 to Lower Olefins

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SSRN 2022年

作者： Gujral, Manleen Kaur Kamesh, Reddi Roy, Anirban Water-Energy Nexus Laboratory Department of Chemical Engineering Goa BITS Pilani Goa 403726 India Process Systems Engineering & Artificial Intelligence Group Process Engineering and Technology Transfer Department CSIR-Indian Institute of Chemical Technology Hyderabad500007 India

In the recent years, the demand for utilisation of CO2 into different chemicals has gathered interest due to increased concerns for global warming. The current work focuses on development of machine learning (ML) framework for modelling and predicting the catalyst activity for direct conversion of CO2 to lower olefins (C2 – C4=) based on the structural-composition-operating parameters. Comprehensive review, and data mining exercise was carried out and input-output data base was formed from 42 relevant reports, including 16 input parameters and catalyst activity (i.e., CO2 conversion (%) & lower olefins (LO) selectivity (%)) as output parameter. Eight ML models were developed including linear, tree-based, kernel-based and artificial neural network (ANN) models, and these models were used to predict the catalyst activity. The performance of developed ML models has been evaluated based on statistical measures. Out of these 8 ML models, ANN model using Bayesian Regularisation (ANN-BR) is able to predict CO2 conversion (%) with less deviation from experimental data (R2 = 0.92, RMSE = 3.86, AAD = 2.5), on the other hand kernel based gradient boosting regression trees (GBRT) resulted as best model for prediction of LO selectivity (%) with R2 = 0.92, RMSE = 8.52, AAD = 3.2 compared to other models investigated in this study. Further, feature important analysis has been carried out to study the influence of input parameters on prediction of catalyst activity. Promoter type and its composition in the catalyst, reaction pressure, and GHSV are found to be significant parameters for prediction of catalyst activity. © 2022, The Authors. All rights reserved.

关键词： Carbon dioxide

Chicken Feather Thermal Decomposition Analysis and Techno-Economic Assessment for Production Value-Added Products Through Continuous Pyrolysis Plant

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SSRN 2022年

作者： Munagala, Chandan Kumar Nagar, Harsha Kesari, Aman Aniya, Vineet Bojja, Sreedhar Process Engineering and Technology Transfer Department CSIR-Indian Institute of Chemical Technology Hyderabad500007 India Hyderabad500075 India Analytical and Structural Chemistry Department CSIR-Indian Institute of Chemical Technology Hyderabad500007 India

The kinetics triplet estimation and economic assessment for a plant-wide design for chicken feathers (CFs) pyrolysis to value added products, char and oil has been conducted. The kinetic estimation showed average activation energy of 204.5 kJmol-1 following a second-order reaction, a three-way diffusion transport mechanism. The CFs pyrolysis has low energy barrier with an enthalpy of 199.6 kJmol-1. An optimum char and oil yield at 550°C with amino acid and its residual groups containing pyrolytic oil was obtained. The economic assessment of 1000 ta-1 of CFs continuous pyrolysis plant has a total capital investment of 302763 USD with an annual production capacity of char and oil was found to be 207.9 ta-1 and 237 ta-1 respectively. The breakeven point was above 67 % of operational capacity with a net profit was 197099 USD and payback period are 2 yr with rate of return 46 % for 90 % operation capacity. © 2022, The Authors. All rights reserved.

关键词： Pyrolysis

Solar thermal heating and freeze concentration for non-centrifugal sugar production: Design and performance analysis

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Energy engineering: Journal of the Association of Energy engineering 2020年第5期117卷 323-342页

作者： Marie, Louis Francois Raj, Sunkara Prudhvi Sai, Policherla Venkata Macleod, Tara Srinivas, Morapakala Srinivas Reddy, K. O’donovan, Tadhg Seán Institute of Mechanical Process and Energy Engineering Heriot-Watt University Edinburgh United Kingdom Department of Mechanical Engineering Birla Institute of Technology & Science Pilani Hyderabad India Heat Transfer and Thermal Power Laboratory Department of Mechanical Engineering Indian Institute of Technology Madras Chennai India Institute of Mechanical Process and Energy Engineering Heriot-Watt University Dubai United Arab Emirates

Non-centrifugal cane sugar (NCS), known as Jaggery, is a form of unre-fined sugar which contains molasses. The integration of renewable energy resources in the production of NCS, have been analysed. The work investigates the energy requirements of a system incorporating a freeze-concentrator and a solar thermal heater to reduce the reliance on the combustion of bagasse or other fuels in a Jaggery production process. Depending on the extent to which freeze concentration can be incorporated into the process, results show that the minimum theoretical energy required to produce Jaggery can be reduced by 91.30% overall. Although difficult in practice, this theoretical analysis demonstrates that the integration of freeze concentration close to the eutectic limit of concentration has significant advantages. For optimal configuration and ideal operation of the system the analysis reports a requirement of approximately 3.8 MJ to produce a kilogram of Jaggery from cane juice. When typical process efficiencies (furnace, boiling pan, solar thermal collector, and chiller) are included, the energy required ranged from 4.8–5.2 MJ/ kg Jaggery. This represents a potential energy saving in excess of 38 MJ/kg Jaggery and a fuel saving of more than 2 kg of bagasse/kg of Jaggery produced. © 2020, Tech Science Press. All rights reserved.

关键词： Potential energy

Unlocking new possibilities in ionic thermoelectric materials: a machine learning perspective

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National Science Review 2025年第1期 195-206页

作者： Yidan Wu Dongxing Song Meng An Cheng Chi Chunyu Zhao Bing Yao Weigang Ma Xing Zhang Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering Mechanics Tsinghua University Key Laboratory of Process Heat Transfer and Energy Saving of Henan Province School of Mechanics and Safety Engineering Zhengzhou University College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education School of Energy Power and Mechanical Engineering North China Electric Power University School of Materials and Chemical Engineering Xuzhou University of Technology

The high thermopower of ionic thermoelectric(i-TE) materials holds promise for miniaturized waste-heat recovery devices and thermal sensors. However, progress is hampered by laborious trial-and-error experimentations, which lack theoretical underpinning. Herein, by introducing the simplified molecular-input line-entry system, we have addressed the challenge posed by the inconsistency of i-TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an R2of0.98 on the test dataset. Using this tool, we experimentally identify a waterborne polyurethane/potassium iodide ionogel with a Seebeck coefficient of 41.39 m V/K. Furthermore, interpretable analysis reveals that the number of rotatable bonds and the octanol-water partition coefficient of ions negatively affect Seebeck coefficients, which is corroborated by molecular dynamics simulations. This machine learning-assisted framework represents a pioneering effort in the i-TE field, offering significant promise for accelerating the discovery and development of high-performance i-TE materials.

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