arXiv subjects

Saba Ghasemi Naraghi

Publications and source records attributed to Saba Ghasemi Naraghi.

3 recordsLinked to original sources

arXiv2026

Toward Decarbonization of Chemical Manufacturing: Joint Optimization of Unit Commitment and Microgrid Operations

The electrification of chemical process heating is essential to industrial decarbonization and sustainable manufacturing of chemical products. Joint optimization of electrified chemical process heating units and electric power systems is needed to achieve decarbonized operation of both sectors. In this work, we introduce a joint optimization model that identifies the optimal unit commitment of power systems and optimal operation of electrified steam cracking microgrids for olefins production. A mixed-integer linear programming (MILP) model is developed to optimize the hourly operational plan of 26 ethylene plants and the main power system in Texas. We propose a two-stage solution method to solve the resulting large-scale MILP problem efficiently. In the first stage, we apply Benders decomposition with LP-relaxed subproblems to decouple microgrid operations from the main power system. In the second stage, we use the first-stage optimal solution as a warm-starting point for the MILP. This two-stage approach reduces the average solution time by 93.5% compared to direct solution of the MILP. Results show that the largest overall greenhouse gas (GHG) emission reduction for both power systems and microgrids is achieved when the electrification level of steam cracking units is at 30%. Above 30% electrification level, a higher electrification level leads to higher overall GHG emissions and steady increase in operating costs, particularly on the microgrid side. Increasing renewables contributions in the electric power system helps debottleneck the electrification efforts and facilitate holistic decarbonization. We also remark that the optimal operational plan of electrified steam cracking microgrids also exhibit strong spatiotemporal patterns.

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arXiv2025

Decarbonization of Steam Cracking for Clean Olefins Production: Optimal Microgrid Scheduling

Ethylene is one of the most ubiquitous chemicals and is predominantly produced through steam cracking. However, steam cracking is highly energy- and carbon-intensive, making its decarbonization a priority. Electrifying the steam cracking process is a promising pathway to reduce carbon emissions. However, this is challenged by the intrinsic conflict between the continuous operational nature of ethylene plants and the intermittent nature of renewable energy sources in modern power systems. A viable solution is to pursue a gradual electrification pathway and operate an ethylene plant as a microgrid that adopts diverse energy sources. To optimize the operational strategy of such a microgrid considering uncertainties in renewable energy generation and market prices, in this work, we propose a novel superstructure for electrified steam cracking systems and introduce a stochastic optimization framework for minimizing the operating costs. Results from a case study show that, given the current status of the power grid and renewable energy generation technologies, the process economics and sustainability of electrified steam cracking do not always favor higher decarbonization levels. To overcome this barrier, electricity from the main grid must be cleaner and cheaper, and energy storage costs per unit stored must decrease. Furthermore, it is important for both chemical and power systems stakeholders must seamlessly coordinate with each other to pursue joint optimization in operation.

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arXiv2024

Stochastic Optimization of Global Agrochemical Supply Chains with Risk Management: Modeling and Reformulation

The global agrochemical market is highly consolidated, with large multinational companies accounting for a major share of the market. Thus, even for a single agrochemical product, its global supply chain typically involves numerous paths connecting the raw material sources to the final customers. Besides structural complexity, agrochemical supply chains are also subject to seasonality and other unique uncertainties, thereby posing a need for risk management tools and strategies. In this study, we model and optimize an agrochemcial supply chain by developing and solving a stochastic mixed-integer quadratic constrained program (MIQCP). We model and control the demand uncertainty in this scenario-based MIQCP using variance. We also apply perspective reformulation techniques to convert the MIQCP to a mixed-integer linear program (MILP). Computational experiment results from an illustrative example show that, successively introducing perspective cuts to the reformulated MILP not only leads to a tight approximation of the original MIQCP model, but is also more computationally efficient than directly solving the MIQCP.

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