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We present a methodology to determine optimal financial parameters in shale-gas production, combining numerical simulation of decline curves and stochastic modeling of the gas price. The mathematical model of gas production considers free gas in the pore and the gas adsorbed in kerogen. The dependence of gas production on petrophysical parameters and stimulated permeability is quantified by solving the model equations in a 3D geometry representing a typical fractured shale well. We use Monte Carlo simulation to characterize the statistical properties of various common financial indicators of the investment in shale-gas. The analysis combines many realizations of the physical model, which explores the variability of porosity, induced permeability, and fracture geometry, with thousands of realizations of gas price trajectories. The evolution of gas prices is modeled using the bootstrapping statistical resampling technique to obtain a probability density function of the initial price, the drift, and the volatility of a geometric Brownian motion for the time evolution of gas price. We analyze the Net Present Value (NPV), Internal Rate of Return (IRR), and Discounted Payback Period (DPP) indicators. By computing the probability density function of each indicator, we characterize the statistical percentile of each value of the indicator. Alternatively, we can infer the value of the indicator for a given statistical percentile. By mapping these parametric combinations for different indicators, we can determine the parameters that maximize or minimize each of them. We show that, to achieve a profitable investment in shale-gas with high certainty, it is necessary to place the wells in extremely good locations in terms of geological parameters (porosity) and to have exceptional fracturing technology (geometry) and fracture permeability. These high demands in terms of petrophysical properties and hydrofracture engineering may explain the industry observation of “sweet spots”

Propane dehydrogenation (PDH) is an on-purpose catalytic technology to produce propylene from propane that operates at high temperatures, 773-973 K. Several key industry players have been active in developing new catalysts and processes with improved carbon footprint and economics, where Pt-based catalysts have played a central role. The optimization of these catalytic systems through computational and atomistic simulations requires large-scale models that account for their reactivity and dynamic properties. To address this challenge, we developed a new reactive ReaxFF force field (2023-Pt/C/H) that enables large-scale simulations of PDH reactions catalyzed on Pt surfaces. The optimization of force-field parameters relies on a large training set of density functional theory (DFT) calculations of Pt-catalyzed PDH mechanism, including geometries, adsorption and relative energies of reaction intermediates, and key C-H and C-C bond-breaking/forming reaction steps on the Pt(111) surface. The internal validation supports the accuracy of the developed 2023-Pt/C/H force-field parameters, resulting in mean absolute errors (MAE) against DFT data of 14 and 12 kJ mol-1 for relative energies of intermediates and energy barriers, respectively. We demonstrated the applicability of the 2023-Pt/C/H force field with reactive molecular dynamics simulations of propane on different Pt surface topologies and temperatures. The simulations successfully model the formation of propene in the gas phase as well as competitive, unproductive reactions such as deep dehydrogenation and C-C bond cleavage that produce H, C1 and C2 adsorbed species responsible of catalytic deactivation of Pt surface. Results show the following reactivity order: Pt(111) < Pt(100) < Pt(211), and that for the stepped Pt(211

This research paper presents a data-driven framework for design optimization of renewable energy communities (RECs) in the residential sector, considering both techno-economic challenges and environmental impact. The study's focus is to determine suitable sizes for photovoltaic systems, wind turbines, and battery electrical energy systems by evaluating energy, economic, and environmental criteria. To achieve this, we develop a data-driven model that incorporates Homer Pro and an in-house tool developed in Python programming language that integrates a machine learning algorithm, life cycle cost (LCC), life cycle assessment (LCA) calculations of the REC model. Furthermore, a multi-objective optimization model is established to minimize the LCC and LCA parameters while maximizing green energy use. Moreover, a multi-criteria decision-making approach based on Weighted Sum Model (WSM) is proposed to help the stakeholders to see beyond the selection criteria based on LCC and LCA to choose the most appropriate scenario optimal solution for the desired energy community and interpret the effect of various economic parameters on the sustainable performance of REC. The framework application is illustrated through a case study for the optimal design of REC for a residential community in Tarragona, Spain, consisting of 100 buildings. The results revealed a substantial improvement in economic , environmental benefits for designing REC, the optimal minimum cost solution with a levelized cost of energy (LCOE = 0.044 $/kWh) and a payback period of 7.1 years with an LCOE reduction of 85.04% compared to the base case. The minimum impact with an LCOE = 0.220 $/kWh and a payback period of 12.5 years with a reduction in environmental impact of 54.59% compared to the base case. Overall, the de