The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model.

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The solubilities of hydrogen halides (HCl, HBr, HI), expressed as xHX moles of HX per mole of a liquid compound, have been determined for a representative selection of compounds at room temperature and below to reveal an essential pattern of behaviour to serve as a guide in operational procedures. Compared with the values predicted by Raoult's law at 0° (xHCl = 0.04, xHBr = 0.09, xHI = 0.37), liquids such as n‐heptane and carbon tetrachloride have slightly lower x values, but show increasing divergence on the low side as the temperature is lowered. Other liquids, such as toluene and alkyl halides, have values greater than those predicted by Raoult's law at 0°, and xHX remains correspondingly higher at the lower temperatures. The xHX values for many liquids can be from 2 to 6 times as great as those for water: xHCl = 0.41, xHBr = 0.49, xHI = 0.39 at 0°. From a consideration of pressure and other effects, a reference pattern of the constitution of the solutions is given. For the non‐aqueous liquids, xHX values greater than Raoult's law values are deemed to be the result of definite chemical reactions involving hydrogen bonding and dynamic equilibria. A case is presented for the desirability of the readjustment of attitudes towards acid‐base function in operational chemistry.

Consumption of fossil fuels, especially in transport and energy-dependent sectors, has led to large greenhouse gas production. Hydrogen is an exciting energy source that can serve our energy purposes and decrease toxic waste production. Decomposition of methane yields hydrogen devoid of COx components, thereby aiding as an eco-friendly approach towards large-scale hydrogen production. This review article is focused on hydrogen production through thermocatalytic methane decomposition (TMD) for hydrogen production. The thermodynamics of this approach has been highlighted. Various methods of hydrogen production from fossil fuels and renewable resources were discussed. Methods including steam methane reforming, partial oxidation of methane, auto thermal reforming, direct biomass gasification, thermal water splitting, methane pyrolysis, aqueous reforming, and coal gasification have been reported in this article. A detailed overview of the different types of catalysts available, the reasons behind their deactivation, and their possible regeneration methods were discussed. Finally, we presented the challenges and future perspectives for hydrogen production via TMD. This review concluded that among all catalysts, nickel, ruthenium and platinum-based catalysts show the highest activity and catalytic efficiency and gave carbon-free hydrogen products during the TMD process. However, their rapid deactivation at high temperatures still needs the attention of the scientific community.