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Thermal energy systems in buildings play a central role in global decarbonization efforts, accounting for a significant share of energy use and carbon emissions. This study addresses a key research question: how can advanced control strategies further enhance the performance of already energy-efficient, low-exergy thermal systems in low-energy buildings? To address this, a model predictive control (MPC) framework is designed to optimize the operation of an advanced thermal system based on modern concepts of low-temperature heating and high-temperature cooling, including ground-source heat pumps, borehole thermal storage, and modern air handling units. This approach employs a multi-layered MPC cost function, considering both immediate operational costs (electricity and heating) as well as system impact penalties, such as CO₂ emissions, thermal energy storage preservation, comfort violations, and peak load shaving, in response to fluctuating market cost signals, outdoor temperature, and thermal storage limitations. Applied to a validated, ultra-efficient commercial building, the MPC framework achieves a 13 % reduction in annual market-responsive operational costs, a 20 % improvement in long-term savings, and a four-year shorter payback period compared to existing well-established rule-based control. The results further confirm the robustness of predictive control under realistic forecast errors, as demonstrated by Monte Carlo simulations. From an environmental perspective, the CO₂ emission index stays below both Swedish electricity and district heating baselines, demonstrating the environmental benefits of predictive control through strategic sector coupling. Beyond the case study, the proposed method provides a scalable pathway for integrating predictive control into next-generation smart buildings. It highlights the potential of MPC as the final optimization layer in advanced thermal systems, aligning with global objectives for cost-promising and carbon-neutral building operations.

Capturing CO2 from waste-fired combined heat and power (w-CHP) plants has attracted increasing attention. However, there has been no method that can guide the operation of CO2 capture for w-CHP plants to attain economic feasibility. To bridge this knowledge gap, this paper proposes a novel method based on the marginal cost of CO2 capture (MCoC) for the operation planning of w-CHP plants at different time scales. Two operating rules (ORs) are considered, which are based on the hourly MCoCs (OR1) and monthly MCoCs (OR2), and a real w-CHP plant is used as a case study. Results reveal that, under the electricity price of 2020 and with a carbon allowance price of 25 & euro;/tonne, the integration of CO2 capture decreased the net revenue by 7.3 million Euro (M & euro;) and 5.4 M & euro; under OR1 and OR2, respectively, compared to the reference plant without CO2 capture. Although OR2 could lead to a lower revenue loss, more CO2 could be captured under OR1. Key factors affecting CO2 capture include the electricity price, the fossil share of waste, the transport and storage cost, the price of carbon allowances, and the price of waste. Higher electricity prices can benefit the w-CHP plant under OR1, while increases in the fossil share of waste, transport and storage costs, and prices of waste decrease the plant's revenue under both ORs. To achieve net revenue from CO2 capture, the price of carbon allowances must exceed the thresholds of 58 & euro;/tonne and 49 & euro;/tonne under OR1 and OR2, respectively.

Integrating CO2 capture into combined heat and power (CHP) plants reduces heat and electricity generation. Heat pumps (HPs) can be utilized to recover waste heat for solvent regeneration or district heating (DH). However, no study compares different ways of utilizing recovered heat. Therefore, this study evaluated the performance of a HP-integrated CHP plant with CO2 capture. Four cases were considered, Case 1 (reference case 1): without CO2 capture; Case 2 (reference case 2): with CO2 capture and no HPs; Case 3: with CO2 capture and using HPs to recover heat for DH; and Case 4: with CO2 capture and using HPs to recover heat for solvent regeneration. Using real operating data from a waste-fired CHP plant (50 MWe, 110 MWth), results demonstrated that, in Case 2 (cf. Case 1), maximum 81.6% of CO2 was captured at a cost of 62.6% reduction in net electricity generation. In Case 3, 45.3% of the DH demand was covered by HP recovered heat, amounting 375.8 GWh/year. In Case 4, 78.3% of the heat required for CO2 capture was supplied by HP recovered heat, reaching 445.1 GWh/year. While 90% of CO2 was captured in Cases 3 and 4, the annual net electricity generation was reduced by 64.5% and 37.1%, respectively. Additionally, given current carbon trading prices, CO2 capture was not economically feasible and that system's internal heat recovery by HPs is not economically feasible, either.

The development of small-scale modular gasification systems represents one of the several pathways needed to follow to reach a net-zero emission society. Currently, the research is fostering the technology readiness level of those units to achieve clean and sustainable energy production from biomass waste while decreasing the load to the centralized waste treatment facilities. The operability of those conversion systems can be improved with the co-gasification of low-grade residues, like sewage sludge, organic municipal solid waste, nut shells, in wood matrixes. Downdraft co-gasification is a promising technology that offers competitive conversion efficiencies, reliability, and capillary diffusion especially in the context of renewable energy communities. Still, there are research gaps that must be addressed especially for improving the design and process control. This work presents a modelling approach that allow for deeper understanding of the treatment and propose improvements for SUPREMAS project. A lumped, steady-state model is developed in Aspen Plus using chemical pseudo-equilibrium and empirical correlations, to replicate the real process. Then, the cleaned syngas is converted to energy with a combined heat and power / solid oxide fuel cell units.

Sewage sludge is one of the main challenges facing the management of municipal solid waste globally. Sludge is the final sink for all the wastes leaving residential, commercial and industrial sources that is conveyed with wastewater and accumulated at the wastewater treatment plants. Sewage sludge can be a good candidate for waste to energy processes like gasification due to energy content. However, tar is challenging the quality of the syngas and needs to be removed using different methods. In this research, an Aspen Plus models is used to study the efficiency of removing tar using water scrubber and thermal cracking. Results from the two techniques will be validated using laboratory experiments and the optimum efficiency will be evaluated based on technical, environmental and economical factors. An experimental procedure to produce representative samples of tar has been established. The physiochemical properties of the tar samples will give the necessary input data for the simulations of the tar removal process optimisation.

Amid the global energy and climate crises, phasing out fossil fuel has become an international priority. Nuclear energy is re-emerging as a fundamental constituent of several countries’ energy mixes. Sweden has updated its policy towards expanding its nuclear energy as a reflection of this global trend, but also due to national political shifts and technological ambitions. In this paper, we explore the current and future nuclear energy’s landscape in Sweden, by examining its historical context and projections. We achieve this by assessing the nuclear energy system through a sustainability lens, considering its four dimensions: feasibility, viability, desirability, and openness. Our analysis shows that, from a feasibility perspective, Sweden’s nuclear energy system must ensure a secure uranium supply and manage its spent fuel. Moreover, it should proactively address climate change impacts, such as sea level rise. From a viability perspective, the system is challenged by long lead times for nuclear plants, though Small Modular Reactors offer a potential solution by reducing costs and risk. Increased public support is in favor of the sector’s desirability, while its reliance on uranium imports puts its security at risk and highlights the critical need to reduce its openness. With a straightforward qualitative assessment, we show the imperative need for a multidisciplinary approach when crafting Sweden’s nuclear policy, to achieve a balance between national energy needs, environmental responsibilities, and the challenges of the global energy market.

Solid oxide electrolysis is a promising technology for green hydrogen production. However, its wide thermal operating range, spanning endothermic, thermoneutral, and exothermic regimes, poses significant challenges for thermal management, particularly during load-following operations. Insufficient control of temperature and thermal gradients can result in substantial thermal stresses, leading to reduced durability and, in severe cases, cracking or failure of cells and stacks. This work investigates the thermal performance of counter-flow and co-flow solid oxide electrolysis cells. A dynamic multi-physics model is developed to simulate coupled electrochemical, thermal, and fluid-flow behaviors. A model predictive control strategy, regulating both cathode and anode flows, is implemented for active thermal control of solid oxide electrolysis cells. The performance of the model predictive control strategy is compared to that of a traditional proportional–integral controller. The results demonstrate that the proposed control strategy outperforms the traditional proportional–integral controller. It effectively maintains the cell temperature at the target value of 800 °C while keeping the maximum thermal gradient across the cell at a moderate level, limited to 6 °C/cm for both co-flow and counter-flow configurations.

In this study calculation over material and energy balances for bio-refinery product upgrading using membrane filtration (MF, UF, and RO), distillation, and ion-exchanger has been performed. Tests have been made with UF filtration in a pilot plant, separation tests made at lab with ion-exchanger and simulation using ASPEN plus simulator for distillation. Rough economic analysis has been made for the different solutions/techniques.

Electric trucks (ETs) offer a potential route towards decarbonizing the transportation sector and reducing transportation-related greenhouse gas emissions. Nonetheless, deploying many ETs brings about operational difficulties regarding optimizing charging strategies which mitigate the cost while guaranteeing stability on grid level. In this paper, we propose a new application of the Lévy Arithmetic Optimization (LAO) algorithm to address the challenges of ET charging schedule optimization. Incorporating high-resolution, real-time load data from Sweden’s electricity grid, the proposed framework dynamically adjusts ET charging power based on grid capacity and electricity prices. The results show that for a fleet of 100 ETs the optimization can give 15.53% savings in total charging costs compared with a baseline scenario. In addition, the method improves state-of-charge (SOC) management and lightens load on the grid, giving scalable, cost-effective and grid-friendly electrification of heavy-duty transportation.

Bioenergy with CO2 capture and storage (BECCS) is an essential component for achieving net-zero CO2 emissions. Existing capture technologies developed for fossil energy-based processes (fossil CCS) can be adapted for BECCS. However, the different properties of biofuels compared to fossil fuels can impact the performance of these technologies. This paper aims to characterize the steps and features of CO2 capture from bioenergy conversion processes to identify the most promising opportunities for BECCS implementation. The paper begins by characterizing the performance of capture technologies used in fossil CCS from both technical and economic perspectives and then summarizes the compositions of the gas streams from various bioenergy conversion processes, including anaerobic digestion, torrefaction, pyrolysis, hydrothermal liquefaction, gasification, and combustion, where CO2 is captured. By exploring the impurity impacts on CO2 capture, from both technical and economic perspectives, and the potential impurities from different processes, recommendations on the selection of CO2 capture technologies are provided for the considered bioenergy conversion processes. It is worth to note that high uncertainties still exist in both energy penalties and capture costs, which highlight the need for demonstration projects.