The unit commitment problem determines the optimal strategy to meet the electricity demand at minimum cost by committing power generation units at each point of time. Solving the unit commitment problem gives rise to a challenging optimization problem due to its combinatorial complexity and potentially long solution time requirements. Our proposed solution approach utilizes a decomposition method in conjunction with alternative models from the EGRET library. Results of this decomposition approach tested against four benchmarking systems show that significant computational speed ups are achieved.

Commodity chemicals and energy supply chains are an essential part of the hydrocarbon industry in several countries. As these supply chains are susceptible to disruptions caused by various risks, the economies of countries that depend on the hydrocarbon sector as a major source of income might be negatively affected. One major risk is the price fluctuations of the resources used in the multiple stages of the supply chains. Investment decisions in this sector aim to secure the investment portfolio's financial returns against the risk of price fluctuations. This work introduces an adaptation of a portfolio optimization technique, the modern portfolio theory (MPT) to the case of commodity chemicals and energy supply chain investments by considering all supply chain stages in formulating the MPT framework. A case study considering four chemical commodities and three potential importing countries is presented with a sensitivity analysis that studies the impact of changing the costs associat... [more]

Transition from fossil fuels to clean energy technologies (CETs) is critical, but material shortages threaten to hinder progress. This study analyzes the potential deficits in 14 key materials – such as lithium, nickel, and cadmium – based on capacity projections for CETs by eight Integrated Assessment Models (IAMs) for 2020-2050. It focuses on technologies including battery storage, concentrated solar power (CSP), electrolyzers, solar photovoltaics (PV), and wind turbines. Our findings show that these materials could face shortages of up to 97% by 2050. To meet rising demand, material production rates must increase sharply, with some materials like cadmium, selenium, and tellurium requiring about 31% increases, peaking in this decade. Immediate actions are needed to accelerate production and improve recycling efforts. However, recycling targets, such as 325% for lithium, seem highly challenging to achieve. Without these measures, material shortages could delay CET deployment, risking... [more]

With increasing emphasis on energy efficiency, more researchers are focusing on energy-efficient flexible job shop scheduling problems. Mathematical programming is a commonly used optimization method for such scheduling challenges, offering the advantages of achieving global optima and serving as a foundation for other approaches. However, current mathematical programming formulations face several challenges, including insufficient consideration of various forms of energy consumption and low efficiency, particularly in handling large-scale instances, which struggle to converge. In this study, we propose a novel global sequence-based approach with high computational efficiency. In this model, immediate precedence relationships are identified using constraints, enabling the precise determination of idle durations within any idle slots. The proposed formulation achieves a significant reduction in energy consumption by up to 20% relative to other formulations. Furthermore, it successfully... [more]

A dual-stack fuel cell model was developed to simulate the hydrogen consumption a fuel cell-powered vehicle for a specific drive cycle. Two fuel cell stacks, each consisting of 65 parallel cells at different aging status and thus with different efficiency profiles (i.e., low and high) were considered. A constrained optimization for power distribution between individual stacks was performed where the objective function was to minimize the hydrogen consumption while meeting the total demand. For proper power management each stack has its own power controller which manipulates the stack current to control the stack power at its desired-set point. Computed optimal power values constitute the desired set-points for the local power PID controllers of the individual stacks. Closed-loop simulations were performed by simulating the developed mechanistic model together with optimization and PID controllers in SIMULINK platform. The closed loop simulations demonstrate how well the power demand of... [more]

The turbulence in energy markets poses risks to energy suppliers, impacting profitability. Whilst risk mitigation is crucial for new projects, adapting existing infrastructure to evolving conditions incurs additional costs. For natural gas dependent economies, the natural gas industry faces exogenous uncertainties represented by demand and price fluctuations, and endogenous risks arising from inadequate proactive planning. This study evaluates the resilience of optimised Qatar’s natural gas monetisation infrastructure under different cases by examining the network’s ability to meet production targets amid process disruptions and market volatility. The analysed network includes 6 direct and indirect utilisation routes, represented by liquefaction, Haber-Bosch, methanol, gas-to-liquids, MTBE and urea processes to produce 9 products. First, process simulations and market assessments were used to obtain operational and market input data. Second, a mixed-integer linear programming model was... [more]

The ongoing energy transition involves decarbonization across different sectors. Amongst these, the transportation sector contributes significantly owing to its reliance on traditional fossil fuels as feedstock. Attaining decarbonization goals requires the adoption of novel sustainable technologies such as electric vehicles (EVs), and hydrogen fuel cell vehicles (HFCVs), amongst others. The feedstock transition towards electricity and dense energy carriers is challenged by the requirement for additional infrastructure to manage intermittency, power generation, and grid expansion which requires both materials and capital investment. By evaluating and redirecting the role of carbon value vector from fossil fuel production towards the production of carbon-based materials such as polymers to empower the energy transition, we can optimize resource allocation and maintain economic viability, all while reducing environmental impact. In this work, we propose an integrated framework to systemat... [more]

The growing size and complexity of energy system optimization models, driven by high-resolution spatial data, pose significant computational challenges. This study introduces methods to reduce model’s size and improve computational efficiency while preserving solution accuracy. First, a composite-curve-based approach is proposed to aggregate granular data into larger resolutions without averaging out specific properties. Second, a general clustering method groups geographically proximate fields, replacing multiple transportation arcs with a single arc to reduce transportation-related variables. Lastly, a two-step algorithm that decomposes the supply chain design problems into two smaller, more manageable subproblems is introduced. These methods are applied to a case study of switchgrass-to-biofuels network design in eight U.S. Midwest states, demonstrating their effectiveness with realistic and detailed spatial data.

Green hydrogen will play a key role in the decarbonization of the steel sector via the direct reduction path [1]. To meet the demand side, both a highly efficient numbering-up based scaling strategy for water electrolysis is needed as well as flexible operation strategies that follow the fluctuating electricity load. This paper presents a modularization approach for electrolysis systems that addresses both aspects by combining different electrolysis technologies into one heterogeneous electrolysis system. We present a modular design of such a heterogeneous electrolysis system that can be scaled for large-scale applications. The impact of different degrees of technological and production capacity-related heterogeneity is investigated using system co-simulation to find an optimal solution for the goal-conflict, that the direct reduction of iron for green steel production requires a constant stream of hydrogen while the renewable electricity profile is fluctuating. For this use-case the d... [more]

Data-driven decision-making is crucial in the transition to a low-carbon economy, especially as global industries strive to meet stringent sustainability goals. Traditional life cycle assessments often rely on static emission factors, overlooking the dynamic nature of the energy grid. As renewable energy penetration increases, grid carbon intensity fluctuates significantly across time and regions, due to the inherent intermittency of renewable sources like wind and solar. This variability introduces discrepancies in emission estimations if time-averaged factors are applied, leading to sub-optimal process operations and unintended environmental consequences. To this end, we present a real-time emission-aware optimization framework, which is implemented through a mixed-integer linear programming formulation that can determine optimal design configurations and operation schedules while simultaneously mitigating emissions by utilizing electricity price forecasts, time-varying emission fact... [more]

The growing demand for sustainable energy has driven research into renewable methane production to reduce greenhouse gas emissions and reliance on fossil fuels. Promising feedstocks include lignocellulosic dry residues, wet waste, and captured CO2, converted via gasification, anaerobic digestion, and synthetic processes with renewable hydrogen. This study uses a multiscale approach to compare these sources, incorporating a techno-economic evaluation to identify key performance indicators (KPI) for facilities and renewable energy sources. A facility location pro- blem (FLP) determines plant locations and production capacities, considering material availability and transportation costs. The analysis focuses on the decentralised use of wastes and CO2 from point and diluted sources across Spain, employing an MILP model to optimise waste and CO2 utilisation alongside solar and wind energy systems. Results highlight lignocellulosic dry waste and CO2 captured with MEA from point sources as th... [more]

This study focuses on optimizing production scheduling in multi-product plants with shared resources and costly changeover operations. Specifically, two main challenges are addressed, the unknown changeover behavior of new products and the need for rapid schedule generation after unforeseen events. An innovative framework integrating Machine Learning (ML) techniques with Mixed-Integer Linear Programming (MILP) is proposed for single-stage production processes. Initially, a regression model predicts unknown changeover times based on key product attributes. Then, a representation where distances correlate with changeover times is compiled through multidimensional scaling, allowing constrained clustering to group production orders according to available packing lines. Ultimately, the MILP model generates the production schedule within a constrained solution space, utilizing optimal product-to-line allocation from cluster segmentation. A case study inspired by a Greek construction material... [more]

Traditional scheduling approaches often rely on simplified process representations to reduce computational complexity, failing to capture the real-world dynamics where tasks often overlap, and their timing depends on finer operational steps. To address these limitations, this paper proposes a novel process representation that breaks down production tasks into smaller, more primitive steps called operations. Unlike traditional methods, this approach provides a more granular depiction of task timing and resource dependencies. Operations can define the start or end of other tasks, utilize shared resources, and incorporate recipe constraints that mandate task sequencing. The proposed representation is utilized to develop two MILP models to address the makespan and the cycle time minimization problems. Finally, the efficiency and practical applicability of the developed models are showcased with a help of a case study from the pharmaceutical industry.

This study introduces a mathematical programming approach to optimize biomass-to-hydrocarbon supply chain design and planning, aiming to balance economic and environmental outcomes. The model incorporates a range of residual biomass types from forestry, sawmills, and the pulp and paper industry, with the option to establish various processing facilities and technologies over a multi-period planning horizon. The analysis involves selecting forest areas, identifying biomass sources, and determining the optimal locations, technologies, and capacities for facilities converting wood-based residues into methanol and pyrolysis oil, which can be further refined into biodiesel and drop-in fuels. Using Life Cycle Assessment (LCA) in a gate-to-gate analysis, forest supply chain carbon emissions are estimated and integrated into the optimization model, extending previous research. A multi-objective framework is employed to minimize CO2-equivalent emissions while minimizing present costs, with effi... [more]

The development of Carbon Capture, Transport, and Storage (CCTS) and hydrogen pipeline networks is crucial for achieving deep decarbonisation in industrial sectors. However, existing network design models often assume perfect foresight, limiting their applicability to real-world infrastructure planning, which is inherently uncertain and iterative. This study introduces a novel rolling-horizon methodology for pipeline network expansion, leveraging a genetic algorithm-based approach that allows for adaptive routing and incremental infrastructure development. By comparing rolling-horizon designs to 2050-optimised networks in a case study of the Humber region in the UK, the analysis highlights the trade-offs between adaptability and cost efficiency. Results indicate that while rolling-horizon approaches better reflect real-world decision-making, they also introduce inefficiencies, increasing capital expenditures by approximately 8% for both hydrogen and CCTS infrastructure. Additionally, t... [more]

The integration of carbon capture and storage (CCS) into coal and biomass co-firing systems (CBCCS) offers a promising solution for reducing carbon emissions in electricity generation. This study evaluates hypothetical scenarios in Malaysia and Indonesia, focusing on techno-economic-environmental transparency. The analysis shows a negligible change in plant net efficiency (~1%) across biomass co-firing ratios of 5-20% in both countries. The capture penalty increases at higher biomass ratios, particularly at 20% co-firing, due to higher auxiliary power demands and steam extraction. As biomass share increases, net CO2 emissions decrease by an average of 43% in Malaysia and 34% in Indonesia. Economic evaluations show a positive revenue increase for Malaysia at a 20% co-firing ratio, while Indonesia faces a revenue deficit (0.6%) under the same condition, mainly due to an unattractive carbon price and feed-in tariff from 2027 onward. Malaysia faces a higher risk of stranded assets due to e... [more]

In this work, a Claus reaction furnace was analyzed in a sulfur recovery unit (SRU) of the Abadan Oil Refinery where the combustion operating temperature is important since it ensures optimal performance in the reactor, this study focused on temperature of control of 1400, 1500 and 1600 K and excess air of 10, 20 and 30% to improve the reaction yield and H2S conversion. The CFD simulation was carried out in Ansys Fluent in transitory state and in 3 dimensions, considering turbulence model ? -e standard, energy model with transport by convention and mass transport with chemical reaction using the Arrhenius Finite – rate/Eddy dissipation model for a Kinetic model of destruction of acid gases H2S and CO2, obtaining a good approximation with experimental results of industrial process of the Abadan Oil Refinery, Iran. The percentage difference between experimental and simulated results varies between 0.5 to 5 % depending on species. The temperature of 1600 K and with excess air of 30% was t... [more]

Despite the potential of electrification in transportation, diesel will remain one of the main fuels for decades. The replacement of diesel with biodiesel is one of the solutions to decrease the net emissions of diesel engines. However, biodiesel has limited performance in cold weather and requires fuel additives. In this context, choosing additives from non-edible, inexpensive, renewable sources is important. Ethyl levulinate, an ester derived from levulinic acid that can be produced from sugarcane, is a promising option because it improves the cold-flow properties of fuels and reduces soot emissions. In this work, a reactive distillation column was designed to produce ethyl levulinate. Because of the volatility order of the components involved in this reaction, levulinic acid was chosen as the excess reactant. Production cost was calculated based on ethanol price, capital cost, and operating expenses for several scenarios. The results showed that the optimized reactive distillation c... [more]

This work develops process options using a novel protonic membrane reformer (PMR) and liquefaction-based CO2 capture process for low-carbon hydrogen production from natural gas. Several hybrid concepts of the PMR and liquefaction process are suggested based on the strategies to handle the residual gas from the reformer. The process intensification and optimization results indicate that the hybrid system with a water-gas-shift reactor and off-gas recycling guarantees high H2 and CO2 recovery rates for the PMR operating at relatively low H2 recovery. The hybrid concept also has 74% energy conversion efficiency, which is higher than a conventional steam-methane reforming (SMR)-based H2 production with chemical absorption CO2 capture.

The rising levels of carbon dioxide (CO2) in the atmosphere significantly contribute to climate change, highlighting the need for effective CO2 mitigation strategies. While capturing and storing CO2 is important, converting it into useful products offers additional environmental and economic benefits. One promising method is the reverse water gas shift (RWGS) reaction, which transforms CO2 into carbon monoxide (CO). Membrane reactors (MR), which integrate selective membranes with equilibrium limited chemical reactions, have the potential to intensify processes based on the RWGS reaction. In such reactors, by-products like water are removed in-situ from the reaction zone, effectively shifting the reaction equilibrium to favor higher CO2 conversion. This study develops a comprehensive multi-scale mathematical model for RWGS membrane reactors. We integrate the microscale permeance model (for LTA-4A membrane) with the RWGS MR unit scale and the system’s scale models. The effectiveness of a... [more]

Direct Air Capture (DAC) technology has gained significant attention as a promising solution for mitigating CO2 emissions and meeting climate goals. However, the current challenges of high energy demand, capital costs, and scalability present critical challenges to the widespread deployment of DAC systems. Integrating DAC with Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings offers a potential solution by enhancing indoor air quality while capturing CO2, thus lowering energy consumption and capital investment compared to standalone DAC systems. This study evaluates the techno-economic performance of an integrated DAC-HVAC system against a standalone DAC system. This analysis combines thermodynamic estimation of CO2 and H2O loadings and energy requirements with an economic evaluation of capital and operating costs to calculate the levelized cost of CO2 capture (LCOD) for both DAC-HVAC and DAC-standalone. A sensitivity analysis explores the effects of varying climat... [more]

A generally applicable framework for the evaluation of the sustainability of distillation processes is proposed by aligning indicators directly to selected sustainable development goals (SDGs) created by the United Nations. The indicators are related to the goals good health and well-being (SDG 3), clear water and sanitation (SDG 6), affordable and clean energy (SDG 7), decent work and economic growth (SDG 8), industry, innovation and infrastructure (SDG 9), responsible consumption and production (SDG 12), climate action (SDG 13) and life below water (SDG 14). A total of 12 sustainability indicators, including human toxicity potential, wastewater generation, water consumption, renewable energy share, energy demand, material footprint, profit, waste generation, recycling ratio of waste, greenhouse gas emission, eutrophication potential and acidification potential are assigned to selected SDGs. The application of the indicators is illustrated by two case studies: a batch (BD) and a conti... [more]

The Life Cycle Assessment (LCA) of a solid oxide electrolyser (SOE) has been performed using publicly available data. The system for producing 1 kg of hydrogen at 25bar and 99.9% purity is represented by a modular structure, which includes the 20-kW solid oxide stack manufacturing, balance of plant equipment, operation consumables, and end-of-life processes. A parametrized life cycle inventory modeling approach was developed. The results illustrate that SOE performs better than steam methane reforming only if supplied by electricity from renewable or nuclear sources. The operation consumables have been identified as the most contributive life stage (67%-89% of potential impacts), followed by equipment manufacturing (7%-22%) and stack manufacturing (4%-11%). Considering the predominant contribution of electricity supply in the consumables, no compromise should be made on ensuring clean electricity sourcing and on the stack energy conversion efficiency. The lifetime of the stack and the... [more]

In this study, we evaluate the economic and environmental impacts of integrating waste polyethylene (PE) pyrolysis with naphtha-based steam cracking for 660 Mt/y ethylene production. We compare six integration scenarios to both business-as-usual (BAU) steam cracking and greenfield waste PE pyrolysis plant. We perform process simulations and equipment design in Aspen Plus® V12, followed by a techno-economic analysis (TEA) and a life-cycle assessment (LCA. The integration capacity we considered corresponds to one full-capacity PE pyrolysis furnace, reducing naphtha feed by 7% in BAU steam cracking. Through the TEA, we identify the most cost-effective scenario by merging the PE pyrolysis gas with the steam cracker furnace outlet after preheating the PE feed. This integration reduces production costs by 6.46MM€/y, improving costs a 0.3% compared to BAU and 30% compared to the pyrolysis greenfield design. LCA results show that the greenfield pyrolysis plant achieves the lowest global warmin... [more]

This study presents a comparative analysis of various configurations for sustainable olefins production via chemical recycling of plastic/biomass wastes, integrating CO2 capture, storage and management technologies. The co-gasification, methanol synthesis and methanol-to-olefins process models were developed on the Aspen Plus® software. Optimization of processing conditions is achieved through the OSMOSE Lua platform, for minimizing the total cost of operation while accounting for seasonal variability in the electricity prices. CO2 valorization processes have been shown to increase carbon efficiency from 55% up to 97% compared to steam naphtha cracking, making chemical recycling of plastics an appealing alternative. In addition, direct CO2 emissions can be fully eliminated, resulting in up to 70% lower net CO2 emissions even when fossil-based plastic waste is used as feedstock. Seasonal CO2 storage can extend the economic benefits by acting as a buffer against high electricity costs an... [more]