The motivation of this work is an application of a production scheduling based on Real-Time Optimization and Zone Control Nonlinear Model Predictive Controller on a liquid recovery unit of an LPG production plant. In this unit, the scheduling-relevant disturbances occur on a time scale relevant to the system dynamics; thus, we propose a novel combination of a well-known control strategies leading to a hierarchical two-layered strategy, where the lower layer employs a zone control nonlinear model predictive controller (NMPC) to define inventory setpoints while the upper layer uses real-time optimization (RTO) to determine optimal plant-wide flow rates from an economic perspective. Unlike a traditional RTO, the proposed upper-layer problem is parameterized by product demands, with a distinct optimization problem formulated for each demand scenario. Our novel approach allows for proactive mitigation of potential inventory issues by dynamically recalculating the distribution of plant produ... [more]

The optimization of shell-and-tube heat exchangers (STHEs) is critical for improving energy efficiency, reducing operational costs, and mitigating environmental impacts in industrial applications. This study evaluates the performance of the Success-Based Optimization Algorithm (SBOA), a novel metaheuristic strategy inspired by behavioral patterns in success perception, against seven established algorithms—Cuckoo Search, Differential Evolution (DE), Grey Wolf Optimization (GWO), Jaya Algorithm, Particle Swarm Optimization, Teaching-Learning Based Optimization, and Whale Optimization Algorithm—for minimizing the total annual cost (TAC) of STHE designs. Using the Bell-Delaware method, the optimization framework incorporates eleven decision variables, including geometric and operational parameters, subject to thermo-hydraulic constraints. A penalty function method enforces feasibility by dynamically adjusting constraint weights. Statistical analysis of 30 independent runs reveals that DE a... [more]

This paper focuses on the control and dynamics of an oil refinery process on an intermediate level - the flows, masses and compositions of and between units within the refining operation. It aims to elucidate optimal strategies for the routing of streams during upset events imposed on the process. A general flowsheet simulation technique including tunable controllers for flows, compositions, levels and reaction extents is incorporated in a Linear Programming model. A standard node represents a mixed receiving tank, with exit streams which can be split, converted and separated. These nodes can be inter-connected arbitrarily in the flowsheet. The method is demonstrated for the case of a planned 3-day shutdown of the catalytic cracker.

This work presents an approach to implement and assess fractional controllers in an extractive distillation system. The experimental dynamic data for an extractive distillation column is used as a case study. A strategy is developed to fit the operation data to fractional-order transfer functions. Then, the fractional controllers are designed in the Simulink environment in Matlab, tuning the controllers through a hybrid optimization approach. First, the approach uses a genetic algorithm to find an initial point, and then the solution is improved through the fmincon algorithm. According to the results of the design of fractional controllers, the sum of the square of errors is below 2.9x10-6 for perturbations in heat duty, and 1.2x10-5 for perturbations in the reflux ratio. Moreover, after controller tuning, a minimal value for ISE of 1,278.12 is obtained, which is approximately 8% lower than the value obtained for an integer-order controller.

Sustainable industrial development depends on optimizing resource and energy integration within Eco-industrial parks (EIPs), combined with stringent carbon emissions reduction policies. The main challenge is ensuring transparency, accountability, and data privacy while optimizing the conversion of raw materials and energy into valuable products and controlling emissions within EIPs. This research introduces an innovative framework to design optimized EIPs and deploy a blockchain-enabled trading platform for resources and emissions management, tackling these key issues. The proposed framework integrates EIPs with emission control policies, supported by two distinct smart contracts: one dedicated to blockchain-based resource trading and another handling financial transactions related to emission control policies, including other regulations such as income tax. The resource trading platform fosters transparency, enabling accurate tracking of material and energy flows. Furthermore, the fra... [more]

Real-world flowshop problems which are very common in the chemical industry are often difficult to solve in a reasonable time with allocation, sequencing, and lot-sizing decisions. Although great progress has been made in the last 20 years regarding MILP model formulations and solution algorithms, realistically-sized flowshop problems with resource and buffer constraints are still difficult to solve. On the other hand, discrete-event simulation (DES) allows for very detailed modelling of process plants, but lacking of optimization capabilities. Simulation Optimization (SO) combines the high-detail DES with mathematical optimization. We show that is possible to integrate MILP and DES using Benders decomposition. We explain the Benders-DES (BDES) approach with a small motivation example with makespan minimization objective and apply it to a real-world case study of a formulation plant with seven formulation and filling lines with sequencing, allocation, and lot-sizing decisions. We show... [more]

In the last decades, the growing awareness of climate change and the high political sensitivity of critical resources such as energy have emphasized a need for local, renewable and optimized energy mixes at various scales. Several studies have therefore aimed to optimize renewable energy technologies and plant locations to develop more renewable and efficient Energy Mixes. Following this trend, this paper applies and compares Goal Programming, Branch-and-Cut and NSGA-II to a multi-objective combinatorial optimization problem focused on the energy mix of Oise, France. Results show more optimality for Goal Programming and Branch-and-Cut, accompanied by a high sensitivity to constraints, while NSGA-II provides more technological diversity in the computed solutions.

Despite the potential benefits of decision-level integration for process and energy supply chains (SCs), these systems are traditionally assessed and optimised by incorporating simplified unit operation models within a spatially distributed network. The desired organisational-level integration cannot be achieved without leveraging complex computational tools and concepts. This work proposes a multi-scale agent-based model to facilitate the transition from traditional practices to coordinated SCs. The proposed multi-agent system framework incorporates different enterprise dimensions of the process and energy SCs, including raw material suppliers, rigorous processing plants, and consumers. The behaviour of each agent type and its interactions are implemented, and their impact on the overall system is investigated. This approach allows for the simultaneous assessment and optimisation of process and SC decisions. By integrating each decision level into the operation, the devised framework... [more]

The battle against climate change and the search for innovative solutions to mitigate its effects have become the focus of the researchers’ attention. One potential approach to reduce the impacts of the global warming could be the design of a Carbon Capture and Storage Supply Chain (CCS SC). However, the high complexity of the model requires exploring alternative ways to optimise it. In this work, a CCS multi-period supply chain for Europe is designed. Data on CO2 emissions have been sourced from the EDGAR database, which includes information spanning the last 50 years. Since this problem involves optimising the cost and operation decisions over a 10-year time horizon, it would be advisable to forecast carbon dioxide emissions to enhance the reliability of the data used. For this purpose, a neural network-based model is implemented for forecasting N-Beats. Furthermore, a temporal decomposition scheme is used to address the intractability issues of the model. The selected method is Lag... [more]

To address the dynamic operational demands of the gas processing sector, which is continuously evolving due to gas market opening, increase in natural gas production, and the growing challenge of upstream-midstream integration in a competitive environment, this work presents the Integrated-Gas-Scheduling-System, IntegraGAS. The proposed methodology innovates by using first principles rigorous process simulation coupled with a scheduling tool for short/medium/long-term, enabling gas plants to swiftly adapt to varying operational conditions and meet the requirements of this new market. IntegraGAS was implemented in Petrobras and has significantly enhanced scheduling efficiency, reducing execution time by up to 99.2% and avoiding approx. US$ 2.3 million in annual labor costs, optimizing resource utilization. By integrating Excel for the frontend, Aspen HYSYS for process simulation, VBA for automation, and Microsoft PowerBI for real-time data visualization, IntegraGAS improves decision-mak... [more]

Renewable carbon sources, such as biomass and waste, are being explored as alternatives for sustainable plastic production. However, the significant uncertainties surrounding the environmental impact of biomass supply processes raise questions about whether these plastics positively contribute to society. Furthermore, the lack of systematic knowledge about plastics and incomplete understanding among stakeholders pose challenges to conducting comprehensive assessments and designing effective plastic life cycle systems. This study aims to clarify the carbon flow within the life cycle of biomass- and recycled-derived plastics and to design a plastic life cycle that enables the introduction of renewable carbon sources. To this end, the study analyzed the structure of plastics containing renewable carbon and conducted a flow analysis of packaging plastics in Japan. The flow analysis was conducted in the form of an optimization problem. Greenhouse gas (GHG) emissions and the proportion of re... [more]

By 2050 around 12% of cumulative emissions reductions will come from Carbon Capture, Utilisation and Storage (CCUS) making it an essential component in the path towards net zero [1]. Focus will initially be on the retrofitting of fossil fuel power plants, which will shift to hard-to-decarbonise industries such as iron, steel, and concrete [1]. Such industries are often grouped together in industrial clusters. Comprising both large and small point sources concentrated over a defined geographical area, industrial clusters offer an opportunity to maximise the impact of CCUS whilst also improving economic feasibility [2]. The North Sea Port (NSP) cluster an example of this. Within the NSP cluster an initial set of five emitters are to join a capture, conditioning, and transport network by 2030. From there other emitters within the area will be able to join incrementally to 2050 [3]. However, the emitters who join and the timing of their connection will have a significant effect on the evo... [more]

Production scheduling in the process industry is an area of significant activity in research and of great practical importance for the performance of industrial companies. In the vast majority of research papers, the scheduling problem is formulated as an off-line problem where a number of jobs is scheduled on a number of resources and the efficiency of the formulation and the solution algorithms is discussed. In reality, however, scheduling is a continuous activity that has to react to the arrival of new orders, to variations in processing times, breakdowns, lack of resources etc. This is termed real-time (or online) scheduling. Available commercial solutions usually provide solutions with relatively long update intervals due to the necessary computation times and a delayed flow of information from the manufacturing execution systems where the data on the current state of the production is collected. Thus the computed schedules are outdated quickly, if not already at the point in time... [more]

This work proposes a two-level model, combining process and supply chain models, and an optimization framework for an integrated biorefinery system to convert agricultural residues into renewable urea via gasification routes. The process model of the gasification, ammonia and urea synthesis was developed in Aspen Plus® to identify key performance indicators such as energy consumption and relative yields for urea for different biomasses and operating conditions; then, these key process data were used in a mixed-integer linear programming (MILP) model, designed to identify the optimal combination of energy source, technological route of urea production and plant location that maximizes the net present value of the system. The model was applied to the whole Brazilian territory, divided into 5569 cities and 558 micro-regions. Each region’s agricultural production was evaluated to estimate biomass supply and urea demand. The Assis microregion, in close proximity with sugarcane and soybean c... [more]

Perishable consumer products like food, cosmetics, and household chemicals face challenges in supply chain management due to limited shelf life and uncertainties in demand and transportation. To address some of these issues, this work proposes a robust optimization framework for jointly optimizing facility allocation and inventory management. The framework determines optimal locations for distribution centers and their assigned customers, as well as inventory policies that minimize the total costs related to transportation, distribution, and storage under uncertain demand in a robust setting. Specifically, we develop a two-stage mixed-integer linear programming (MILP) model is that incorporates First-In-First-Out (FIFO) inventory policy to reduce spoilage. The bilinear FIFO constraints are linearized to improve computational efficiency. Social equity is integrated by defining a fairness index and incorporating it in facility allocation. Demand uncertainty is tackled using a robust opti... [more]

This study aims to develop an optimization-based approach to design the carbon capture, utilization, and storage (CCUS) supply chain and analyze the optimal configuration and investment strategies. To achieve this goal, we develop an optimization model that determines the logistic decision-making to maximize the net present value (NPV) and minimize the net CO2 emissions (NCE) of the strategies of the CCUS supply chain under logical and practical constraints. We estimate the technical (production scale and energy consumption), economic (capital and operating expenditure), and carbon-related (CO2 emissions) parameters based on the literature. By adjusting major cost drivers and economic bottlenecks, we determined major decision-making problems in the CCUS framework, such as sequestration vs. utilization. As a real case study, the future CCUS system of South Korea was evaluated, which includes three major CO2 emitting industries in South Korea (power plants, steel, and chemicals), as well... [more]

Fossil fuel reliance in the transportation sector remains a leading contributor to global greenhouse gas emissions, underscoring the urgent need for renewable alternatives like biodiesel. Derived from renewable feedstocks, biodiesel can reduce emissions, enhance energy independence, and support rural economies. However, its production faces challenges such as low energy efficiency, process optimization barriers, and the limited utilization of byproducts like glycerol, which elevate costs and hinder large-scale adoption. This study addresses these challenges by developing an integrated framework for biodiesel production and byproduct valorization, supporting the long-term decarbonization of biofuel production. Three key feedstocks—refined palm oil, rapeseed oil, and soybean oil—are evaluated for biodiesel yield. The single-step transesterification process is enhanced through a two-stage approach, optimizing fatty acid methyl ester conversion under varying methanol and NaOH catalyst spli... [more]

Deriving accurate cost projections associated with producing hydrogen within the context of an energy-export paradigm is a challenging feat due to non-deterministic nature of weather systems. Many research efforts employ deterministic models to estimate costs, which could be biased by the innate ability of these models to ‘see the future’. To this end we present the findings of a multistage stochastic model of hydrogen production for energy export (using liquid hydrogen or ammonia as energy vectors), the findings of which are compared to that of a deterministic programme. Our modelling found that the deterministic model consistently underestimated the price relative to the non-deterministic approach by $ 0.08 – 0.10 kg-1(H2) (when exposed to the exact same amount of weather data) and saw a standard deviation 40% higher when modelling the same time horizon. In addition to comparing modelling paradigms, different grid-operating strategies were explored in their ability to mitigate three... [more]

The urgent need to mitigate climate change and reduce reliance on fossil fuels highlights green hydrogen as a key component of the global energy transition. This study assesses the feasibility of producing hydrogen offshore using wind energy, focusing on economic and environmental aspects. Offshore wind energy offers several advantages: access to water for electrolysis, potentially lower hydrogen export costs compared to electricity, and storage systems that stabilize wind energy output. However, significant challenges remain, including the high costs of storage solutions, capital expenditures (CAPEX), and operational costs (OPEX). A Mixed-Integer Linear Programming (MILP) model optimizes the production units, storage, and distribution processes. A case study in southern France examines hydrogen production from a 150 MW floating wind farm. While hydrogen produced from offshore wind ranks among the most environmentally friendly, its costs remain high, and production volumes fall short o... [more]

Green hydrogen is widely recognized as a key player in the decarbonization of the energy system. To transport it efficiently, hydrogen must be converted into a carrier, such as liquefied hydrogen or ammonia, to increase its volumetric density. The supply chain of these carriers includes hydrogen conversion into the carrier, overseas transport, and carrier reconversion back to hydrogen. A case study involving hydrogen transportation across the Mediterranean Sea is used to evaluate the carrier efficiency. The processes involved in the supply chain are simulated in Aspen Plus® V11 to determine material and energy balances, and the "net equivalent hydrogen" method is applied to calculate the equivalent amount of hydrogen needed to supply thermal or electric power. The efficiency, defined as the ratio of net hydrogen delivered (after accounting for consumption and boil-off losses) to the initial hydrogen input, is higher for ammonia than for liquefied hydrogen (73% vs 60%, respectively). Th... [more]

In response to global efforts to reduce carbon emissions, the oil refining sector, a major source of industrial emissions, has set ambitious decarbonization targets. This study analyzes trade-offs between minimizing CO2 emissions and costs through the use of Pareto optimal solutions. A superstructure optimization framework evaluates various technological pathways and timelines, employing a bi-criterion optimization approach using the ?-constraint method. Results show that cost-effective, higher-emission solutions often involve natural gas-based technologies with carbon capture, while expensive, low-emission solutions favor electric power-based technologies. The analysis incorporates detailed assumptions about grid carbon intensity of varying degrees and accounts for varying national policies. Comparative case studies across locations highlight how grid carbon profiles influence optimal strategies, providing insights to inform local policies and incentivize technologies.

Olefins are essential precursors in producing a wide range of chemical products, including plastics, detergents, adhesives, rubber, and food packaging. Ethylene and propylene are the most ubiquitous olefin components and are predominantly produced through steam cracking. However, steam cracking is highly energy- and carbon-intensive, making its decarbonization a priority as the energy sector shifts toward clean, renewable electricity. 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 (e.g., solar and wind) in modern power systems. Massive energy storage systems or full plant reconfigurations to meet the power demand of electrified crackers are shown to be economically and practically infeasible. Thus, a more viable solution is to pursue a gradual electrification pathway and ope... [more]

Superstructure based approaches have long been employed for optimal process synthesis problems. Due to the difficulties of using rigorous process models and simultaneous solutions, shortcut calculations have been the preferred means of modeling unit operations within larger process network problems. However, even with the use of shortcut equations to model the behaviour of unit operations, the resulting mixed-integer programs can be challenging to solve. Furthermore, generating the problem superstructure has often been done manually, presenting issues for scaling to larger problems. We demonstrate the use of an algorithmic approach to generate network superstructures for synthesis problems coupled with equation reformulations to yield an MIQCP (Mixed-Integer Quadratically Constrained Program) for networks of thermally coupled distillation columns. The combination of rapid problem generation with the ability to leverage recent advances in the performance of QCP (Quadratically Constraine... [more]

With an increasing share of non-dispatchable renewable energy sources in the European grid, energy flexibility will be key for the industrial sector to support the green transition. The EU-project Flex4Fact aims at finding solutions for energy and process flexibility for industry, using SINTEF’s open-source energy system model EnergyModelsX to quantify the potential benefits. This work presents some extensions done in EnergyModelsX, denoted as EnergyModelsFlex, to accommodate energy and industrial flexibility, adding new functionalities to assist with industrial flexibility potential. The extended EnergyModelsX model is described and demonstrated through two case studies in the plastic and polymeric products manufacturing sector to evaluate their potential for increasing renewable generation and flexibility. The first use case, being energy intensive, consumes both natural gas and electricity. This site enables the use of heat recovery and utilization, hydrogen blending, on-site hydrog... [more]

A key technology for integrating fluctuating renewable energy into the process industry is the production of green hydrogen through water electrolysis plants. Scaling up electrolysis plant capacity remains a significant challenge for the renewable energy transition. System simulation of large-scale electrolysis plants can support process design, monitoring, optimization, and maintenance scheduling. Hybrid modeling methods are promising for improving simulation reliability by combining process knowledge with process data, addressing gaps in understanding of the underlying processes. These hybrid, semi-parametric models have shown improved accuracy than purely mechanistic models. This study develops a hybrid, semi-parametric model for an anion exchange membrane electrolysis (AEMEL) stack unit. Parameters such as heat loss and heat transfer, which cannot be directly measured, are estimated using real process data. Sensors provide data on lye tank temperature, outlet temperature, and flow... [more]