Stochastic Modeling and Techno-Economic Evaluation of Renewable Energy Integration in Petrochemical Processes

Abstract

Paraschos Maniatis* Athens*

The petrochemical industry, a cornerstone of modern economies, faces increasing pressure to decarbonize its operations due to environmental concerns and evolving regulatory landscapes. Integrating renewable energy sources (RES) into these energy-intensive processes offers a promising pathway towards sustainability. However, the inherent variability and intermittency of RES, coupled with the complex and continuous nature of petrochemical production, introduce significant challenges and uncertainties.

This research paper presents a comprehensive stochastic modeling framework for the techno-economic evaluation of renewable energy integration in petrochemical processes. We employ advanced statistical methods, including Monte Carlo simulations and scenario analysis, to quantify the impact of uncertainties associated with renewable energy availability, energy market fluctuations, and operational parameters on system performance and economic viability. The framework Incorporates detailed models of various RES (e.g., solar PV, wind), energy storage systems, and their interaction with existing petrochemical plant utilities and processes. A key focus is on hypothesis testing to rigorously assess the economic feasibility and environmental benefits of different integration strategies. Research results demonstrate the critical role of stochastic modeling in identifying optimal integration pathways that minimize operational risks and maximize economic returns while significantly reducing greenhouse gas emissions. The findings provide valuable insights for policymakers, industry stakeholders, and researchers in navigating the complex transition towards a more sustainable petrochemical sector.

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