This paper presents a Europe-wide optimisation framework for designing and operating a multi-period Carbon Capture and Storage (CCS) supply chain across Europe. A MATLAB preprocessing pipeline constructs an auditable techno-economic dataset (emission nodes, ports, aquifers, candidate pipeline/shipping arcs and costs) and exports it to a GAMS optimisation model. The planning problem is formulated as a two-stage stochastic MILP, where scenario-independent first-stage decisions select discrete pipeline and shipping capacity bands and port operating modes, while scenario-dependent second-stage decisions allocate capture, transport and sequestration flows. Uncertainty is represented through correlated scenarios of capture unit costs for four capture technologies (CV=0.35, rho=0.8, Ns=20). To address the computational burden induced by inter-temporal binary investments and scenario replication, we apply a two-phase arc-screening heuristic: an LP relaxation on the full network identifies prom... [more]

While classical heuristics can be applied to decide on the preferred reactor concept for simple reaction schemes, more complex reaction networks require more sophisticated methods, such as the multilevel reactor design approach or superstructure optimization. Based on an analysis of the existing methods a nonlinear programming framework for a superstructure-based reactor network synthesis is presented, emphasizing numerical robustness and flexible network representation without relying on integer decisions. The approach, which is implemented in GAMS, allows for the combination of continuous stirred-tank and cross-flow reactor models. An exemplary application for the classical Van de Vusse reaction is first shown for validation, prior to the application to an enzymatic cascade based on the Weimberg pathway. Assuming fast co-factor regeneration, the performance of the resulting PFR cascade, which can also be interpreted as a sequence of batch reactions, is compared with a commonly applie... [more]

We present a steady-state, optimization-based techno-economic study of a continuous fluidized temperature-swing adsorption (TSA) system for post-combustion CO2 capture from biomass-derived flue gas, using two adsorption stages and one desorption stage with integrated heat-pump thermal management. The GAMS/CONOPT4 model couples molar and energy balances, Toth adsorption equilibrium, fluidized-bed hydrodynamics and literature cost correlations. Optimization yields CO2 purity of 96% v/v and 95.5% recovery at low, safe pressures with particle Reynolds numbers of 2-11, indicating near-minimum-fluidization operation. The nominal capture cost is 87 USD/tonCO2 with an internal rate of return of 42%; utilities comprise 49% of annualized costs and the adsorption compressor dominates equipment capital. Disabling the heat pump increases modeled capture cost to 124 USD/tonCO2, highlighting the heat pump's decisive role in reducing energy demand and costs. Adding adsorption stages lowers modeled cos... [more]

The increasing popularity of electric vehicles (EVs) leads to an expected rise in the quantity of end-of-life lithium-ion batteries (LIBs) that require efficient management. This paper presents a State Task Network (STN) based optimization model to analyze and optimize the supply chain for LIBs, allowing for the selection of optimal processing routes, facility locations, capacities and reintegration of recovered materials, as well as analyzing the possible trade-offs between different end-of-life management strategies. Based on available data from the literature, the model is demonstrated with the LIB supply chain considering both primary production and different end-of-life strategies for spent LIBs (recycling and reuse). The case study reveals that mechanical pretreatment followed by hydrometallurgical recycling is the optimal pathway and it outperforms the linear supply chain in both costs and emissions. The cost optimal solution opts for more centralized collection and disassembly,... [more]

CCS is a well investigated and fairly promising technology for reducing the emission of carbon dioxide (CO2) to the atmosphere. However, it is rarely implemented in the industry due to its high cost. Therefore, this work proposes a cost optimized CCS chain which can be operated flexibly and safely. For the capture process a post combustion chemical absorption technology is chosen due to its retrofitting possibility to already existing power plants and its low capture cost. In order to find a cost efficient absorption process for different scenarios, the five most promising process configurations from previous work are combined into a superstructure in a rigorous rate based reactive Aspen Plus model. This in turn is optimized by a two-stage stochastic programming approach in Matlab. The optimal supply chain network is identified by a tailor made transshipment model implemented in GAMS, which accounts for the most promising transportation units, storage sites as well as direct utilizatio... [more]

GAMS models and supporting spreadsheets for Innovative Strategies in Sustainable Formaldehyde Production in Belgium: Integrating Process Optimisation, Carbon Capture, and a comprehensive Environmental Assessment.

UCL Chemical Engineering ensures graduates are digitally literate by integrating computational tools like gPROMS, Aspen Plus, and GAMS into the undergraduate curriculum. Students in the first year of undergraduate program use GAMS to solve simple simulation and optimization problems and gPROMS for solving ordinary differential equations (ODEs) in reactor design problems. In the second year, students start using Aspen Plus to simulate more complex chemical process units, interpret and discuss results obtained and justify any differences observed between experimental data and computational results. They use GAMS to simulate and optimize a process flowsheet with considerations of the implications of proper initialization procedures and strategies for obtaining optimal parameters and gPROMS for advanced reactor and separator problems. The computational knowledge acquired in the first two years prepares students for the third-year capstone design project where they use the various tools in... [more]

The production of hydrogen from solar energy has surged in popularity in recent years, driven by global initiatives to combat climate change. The Greater Middle East (GME) region, with its favorable geographical position, offers considerable potential for solar-based hydrogen generation. This study combines Geographic Information System (GIS) spatial analysis and the Analytical Hierarchy Process (AHP) with data-driven optimization models to assess land suitability and hydrogen production potential within the region under various scenarios. Findings highlight that water availability is the primary limiting factor, followed closely by road accessibility in determining land suitability for hydrogen production. According to the AHP analysis, only 3.8% of the GME region is highly suitable for such initiatives. Projections suggest that by 2050, the region could achieve a total hydrogen production capacity of up to 1590 Mt/y, potentially avoiding around 4586 Mt of CO2 emissions if all highly... [more]

By capturing carbon dioxide from biomass flue gases, energy processes with negative carbon footprint are achieved. Among carbon capture methods, the fluidized temperature swing adsorption (TSA) column is a promising low-pressure alternative, but it has been developed on small scales. This work aims to model, simulate and optimize a fluidized TSA multi-stage equilibrium system to obtain a cost estimate and a conceptual design for future process scale up. A mathematical model described adsorption in multiple stages, each with a heat exchanger, coupled to the desorption operation. The model was based on elementary macroscopic molar and energy balances, coupled to pressure drops in a fluidized bed designed to operate close to the minimum fluidization velocity, and coupled to thermodynamics of adsorption equilibrium of a mixture of carbon dioxide and nitrogen in solid sorbents (the Toth equilibrium isotherm was used). The complete fluidized TSA process has been optimized to minimize costs,... [more]

Inadequate gas supply is partly responsible for the energy shortfall experienced in some energy-poor nations. Favorable market conditions would boost investment in the gas supply sector; hence, we propose a blockchain-based fair, transparent, and secure gas trading scheme that facilitates peer-to-peer trading of gas. The scheme is developed using an Ethereum-based smart contract that receives offers from gas suppliers and bid(s) from the thermal plant operator. Giving priority to the cheapest offers, the smart contract determines the winning suppliers. This paper also proposes an economic dispatch model for gas-deficient plants. Conventional economic dispatch seeks to satisfy electric load demand whilst minimizing the total gas cost of generating units. Implicit in its formulation is the assumption that gas supply to generating units is sufficient to satisfy available demand. In energy poor nations, this is hardly the case as there is often inadequate gas supply and conventional econom... [more]

Source code for the case study presented in the paper "Approximating Nonlinear Relationships for Optimal Operation of Natural Gas Transport Networks". The case study involves solving the compressor fuel cost minimization problem (FCMP) on three simple natural gas networks. For each gas network three different formulations of the FCMP are tested: a common simplified FCMP model (FCMP_S), the novel approximation FCMP model (FCMP_N) that is developed in the paper, and a partially rigorous FCMP model (FCMP_PR) that models components of the model using their most rigorous calculations where feasible. The FCMP for each of these tests was optimized using GAMS, for which the code is provided. The accuracy of each of the three models was then assessed by comparing them to a rigorous simulation. The rigorous simulation was coded in Matlab and is provided, where separate files are used to calculate the rigorous gas pressure drop along a pipeline, and the energy input required for gas compression... [more]

Artificial neural networks (ANNs) are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of ANN embedded optimization problems. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced-space [\textit{SIOPT}, 20 (2009), pp. 573-601] including the convex and concave envelopes of the nonlinear activation function of ANNs. The optimization problem is solved using our in-house global deterministic solver MAiNGO. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process optimization. The results show that computational solution time is favorable compared to the global general-purpose optimization solver BARON.