Using Bayesian optimization and ensemble boosted regression trees for optimizing thermal performance of solar flat plate collector under thermosyphon condition employing MWCNT-Fe3O4/water hybrid nanofluids

Improving the thermal efficiency of a solar flat plate collector using MWCNT-Fe3O4/water hybrid nanofluids and ensemble machine learning

Using machine learning approaches to model and optimize a combined solar/natural gas-based power and freshwater cogeneration system

In the future, when fossil fuels are exhausted, alternative energy sources will be essential for everyday needs. Hydrogen-based energy can play a vital role in this aspect. This energy is green, clean, and renewable. Electrochemical hydrogen devices have been used extensively in nuclear power plants to manage hydrogen-based renewable fuel. Doped zirconate materials are commonly used as an electrolyte in these electrochemical devices. These materials have excellent physical stability and high proton transport numbers, which make them suitable for multiple applications. Doping enhances the physical and electronic properties of zirconate materials and makes them ideal for practical applications. This review highlights the applications of zirconate-based proton-conducting materials in electrochemical cells, particularly in tritium monitors, tritium recovery, hydrogen sensors, and hydrogen pump systems. The central section of this review summarizes recent investigations and provides a comprehensive insight into the various doping schemes, experimental setup, instrumentation, optimum operating conditions, morphology, composition, and performance of zirconate electrolyte materials. In addition, different challenges that are hindering zirconate materials from achieving their full potential in electrochemical hydrogen devices are discussed. Finally, this paper lays out a few pathways for aspirants who wish to undertake research in this field.

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A Review of Applications, Prospects, and Challenges of Proton-Conducting Zirconates in Electrochemical Hydrogen Devices

Optimization of combustion, performance, and emission characteristics of a dual-fuel diesel engine powered with microalgae-based biodiesel/diesel blends and oxyhydrogen

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required. 

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Cost benefit analysis is a systematic approach for calculation and analyzing the cost of a project. Soft computing approaches are also applicable to deal with cost benefit analysis. In this paper Mamdani fuzzy system has been developed for cost benefit analysis. The genetic optimization of the model is carried out. The interpretability and accuracy features are also analyzed.

Developing a Genetic Fuzzy System Model for Cost-Benefit Analysis.

The synthesis of branched hydrocarbons for high-octane gasoline and sustainable aviation fuel directly from CO2-rich syngas in a single reactor holds potential to decrease capital and operating costs and increase overall energy and carbon efficiencies in a biorefinery. Here, we report the cascade chemistry of syngas to hydrocarbons under mild reaction conditions in a single reactor with C4+ single-pass yields of 13.7–44.9%, depending on the relative catalyst composition employing our dimethyl ether homologation catalyst, Cu/BEA zeolite. With co-fed CO2 at a concentration representative of biomass-derived syngas, 2.5:1:0.9 for H2:CO:CO2, a hydrocarbon yield of 12.2% was observed with similar selectivity to C4+ products compared to the CO2-free feed. Definitive evidence of CO2 incorporation into the hydrocarbon products was demonstrated with isotopically labeled 13CO2 co-feed experiments, where mass spectrometry confirmed the propagation of 13C into the C4+ hydrocarbons, highlighting the feasibility to co-convert CO and CO2 in this single reactor approach. 

Direct Conversion of Renewable CO2-Rich Syngas to High-Octane Hydrocarbons in a Single Reactor

Biodiesel manufacturing from renewable feedstocks has received a lot of attention as a viable alternative to fossil fuels. The Box-Behnken design, analysis of variance (ANOVA), and the Grey Wolf Optimizer (GWO) algorithm were used in this work to optimise biodiesel production from Nahar oil. The goal was to determine the best operating parameters for maximising biodiesel yield. The Box-Behnken design is used, with four essential parameters taken into account: molar ratio, reaction duration and temperature, and catalyst weight percentage. The response surface is studied in this design, and the key factors influencing biodiesel yield are discovered. The gathered data is given to ANOVA analysis to determine the statistical significance. ANOVA analysis is performed on the acquired data to determine the statistical significance of the components and their interactions. The GWO algorithm is used to better optimise the biodiesel production process. Based on the data provided, the GWO algorithm obtains an optimised yield of 91.6484% by running the reaction for 200 minutes, using a molar ratio of 7, and a catalyst weight percentage of 1.2. As indicated by the lower boundaries, the reaction temperature ranges from 50 °C. The results show that the Box-Behnken design, ANOVA, and GWO algorithm were successfully integrated for optimising biodiesel production from Nahar oil. This method offers useful insights into process optimisation and indicates the possibilities for increasing the efficiency and sustainability of biodiesel production. Further study can broaden the use of these strategies to various biodiesel production processes and feedstocks, advancing sustainable energy technology.

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Anurag Kumar

Papers

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Developing a Genetic Fuzzy System Model for Cost-Benefit Analysis.

Direct Conversion of Renewable CO2-Rich Syngas to High-Octane Hydrocarbons in a Single Reactor

Optimization of biodiesel production from Nahar oil using Box-Behnken design, ANOVA and grey wolf optimizer