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Thermoacoustic technology emerges as a sustainable and low-carbon method for energy conversion, leveraging environmentally friendly working mediums and independence from electricity. This study presents the development of a multimode heat-driven thermoacoustic system designed to utilize medium/low-grade heat sources for room-temperature cooling and heating. We constructed both a simulation model and an experimental prototype for a single-unit direct-coupled thermoacoustic system, exploring its performance in heating-only, cooling-only, and hybrid heating and cooling modes. Internal characteristic analysis including an examination of internal exergy loss and a distribution analysis of key parameters was first conducted in the hybrid cooling and heating mode. The results indicated a positive-focused traveling-wave-dominant acoustic field within the thermoacoustic core unit, enhancing energy conversion efficiency. The output system performance was subsequently tested under different working conditions in the heating-only and cooling-only modes. A maximum output heating power of 2.3 kW and a maximum COPh of 1.41 were observed in the heating-only mode. Meanwhile, a cooling power of 748 W and a COPc of 0.4 were obtained in the typical cooling condition at 7 °C when operating in cooling-only mode. These findings underscore the promising potential of thermoacoustic systems for efficiently utilizing medium/low-grade heat sources for cooling and/or heating applications in the future.

This paper proposes a novel online and self-learning algorithm to the identification of fuzzy neural networks, which not only learns the structure and parameters online but also learns the threshold parameters by itself and automatically. For structure learning, a self-constructing approach including adding neurons and merging highly similar fuzzy rules is proposed based on the criteria of the system error between actual and model output and the maximum firing strength of neurons. In order to achieve the efficient merging computing, a new calculation method of similarity degree between fuzzy rules is developed. Further and more importantly, the varying width of Gaussian membership functions can be learned by itself according to the underfitting and overfitting criteria. Similarly, different from the existing constant threshold of similarity degree for merging, the varying threshold of similarity degree can be self-learned according to the real-time accuracy of model. The proposed self-learning mechanism significantly improves the model accuracy and greatly enhances the easy usability. Several benchmark examples are implemented to illustrate the effectiveness and feasible of the proposed approach.

Network temperatures in district heating systems are important operational factors for obtaining efficient performance. A low network return temperature allows for the recovery of low-grade heat from assets such as condensing boilers, waste incineration, geothermal sources and industrial waste heat. Fluctuations in heating and cooling demands affect the return temperatures of the building substations and in the network. This variability impacts the economic viability and environmental sustainability of the entire system. This paper presents a nonlinear optimization strategy to maintain sufficient energy flows in the network's primary and secondary circuits to achieve low return temperatures from all substations in the network. The defined optimization strategy incorporates the thermodynamic model of the substation and building heating system as opposed to traditional weather-based supply temperature adjustments. The estimated heat demands and tariffs, CO2 penalties are inputs used by the optimizer to find theoptimal solution. The total operational expenditure for electricity and gas consumption shows an 18% reduction with 8% reduction in emissions and 6% efficiency improvement when compared with the measured weather-based approach. The developed strategy will aid the network operators in the economic dispatch of heat generation while ensuring the user's thermal comfort.

Abstract: To maximize the sustainable and economic benefits of collective heating systems, proper sizing is fundamental. This paper presents the validation of a novel sizing approach for collective systems producing and distributing heat for both space heating and domestic hot water, utilizing residential heat meter data. A validation methodology is developed to overcome the limitations of this type of data to identify the peak heat demand and estimate the peak heat demand under design outdoor conditions. The latter is estimated utilizing multiple linear regression coupled with an analysis of the maximum deviations. The power-storage characteristic, which shows all combinations of thermal power and thermal storage to meet the peak heat demand is determined and used to validate the novel sizing approach for six case studies. Although the results are promising, undersizing problems may arise in cases with decentralized heat storage

Abstract: To achieve net-zero emissions by 2050, as outlined in the European Green Deal, nuclear power is expected to double between 2020 and 2050, mainly due to its low-carbon baseload capacity. Small modular reactors, new nuclear reactors designed to generate up to 300 MW of electricity, could help achieve this goal. Small modular reactors have unique advantages over existing large reactors, such as modularization, learning and co-location economics. However, these small modular reactors should also be economically viable. This review therefore focuses on the costs of small modular reactors. This review found an average capital cost of €7,031/kW and an average levelized cost of electricity of 85 €/MWh for small modular reactors, while capital costs were found to be on average 41% higher than for the large reactors. Carbon and gas prices are not included in this cost estimate, yet these volatile prices also affect small modular reactor costs. However, as the absolute cost is lower, the financial risk is lower for small modular reactors. The importance of regulations, discount rates, country and project specifications and public acceptance are also considered.

Vacuum pressure swing adsorption (VPSA) has demonstrated promising features for the upgrading of biogas to biomethane. In this study, a biogas upgrading plant, comprised of a hybrid of an N-column VPSA unit (2 ≤N≤ 6) with a combined heat and power (CHP) engine, was developed and its techno-economic characteristics were assessed via a mathematical approach. Moreover, the techno economic analysis was used for the state-of-the-art VPSA configuration (a sophisticated configuration) and compared with the developed hybrid process. The prominent parameters including feedstock transport, biogas production, desulfurization, drying, upgrading, combustion, and grid injection were considered in the analyses of the plant for the upgrading capacity in the range of 100 - 6,500 Nm3/h. Sensitivity analysis of the most influencing parameters, i.e., electricity price, gas price, and feed processing revenues, was conducted for the developed models. Beside comparing upgrading cost of the sophisticated VPSA with other upgrading technologies, a detailed comparison with the best available membrane unit for biogas upgrading was conducted. The limitations of adsorption process and VPSA in reducing the upgrading cost were also investigated. The results showed that in the absence of subsidies and requirements for CO2 capture, the hybrid plant outperforms the sophisticated VPSA units. Also, higher market price of natural gas or feedstock processing revenues were necessary in order to render the plant profitable. The results showed that, at flowrates larger than 175 Nm3/h, the sophisticated VPSA unit required a lower investment cost than the membrane unit for identical outputs. The results also show that even at the most idealistic conditions in the adsorption process, the upgrading is not economically favorable without subsidies. The findings of this study shed light on the importance of process design for biogas upgrading.

Abstract The aim of this study is to evaluate the incremental innovation performance of nuclear energy projects. Within this context, a novel model is generated which consists of two different stages, and large nuclear reactors are taken into consideration. Firstly, the Pythagorean fuzzy DEMATEL is used to weight the phases of technology S-Curve for nuclear energy projects. Moreover, the second stage includes the ranking two-generation technology S-curve with integer patterns for nuclear energy projects. In this framework, the best combinations are selected for innovation life cycle pattern with the integer code series. The findings demonstrate that the nuclear energy companies need to consider the two-generation technology S-Curve because continuous technological developments are occurring for nuclear power generation. It is also determined that aging in the first generation is the most significant period of two-generation technology S-Curve for nuclear energy projects. In this process, critical decisions should be made regarding future technological investments. In addition, the growth phase in the second generation is also important for the effectiveness of the nuclear energy technology. Conducting effective evaluations in these processes will contribute to increasing the efficiency of companies.

The aim of this study is to define strategies to increase green nuclear energy investments. Within this context, a model is suggested that includes two different stages. Firstly, the consensus-based fuzzy preferences are determined for these investments. For this purpose, four different TRIZ-based strategies are identified, which have an influence on the effectiveness of these projects. Furthermore, in the second stage, the significance weights of these strategies are calculated by considering the spherical fuzzy DEMATEL approach. The findings indicate that dynamicity is the most appropriate TRIZ strategy for the effectiveness of green nuclear energy investments. In addition, the prior action is another significant strategy that should also be considered in these projects. It is obvious that sufficient technological infrastructure is very important for the performance of green nuclear power plants and there is a rapid development of this technology. Because of this situation, these nuclear power plants should be designed effectively so that they can be adoptable to any changes in the technology. In this scope, qualified personnel should be employed, and communication between departments should be effective and of high quality. With the help of these issues, technological changes can be adopted more easily.

Abstract: Second-generation biofuels, starting from lignocellulosic biomass, are considered as a renewable alternative for fossil fuels with lower environmental impact and potentially higher supply and energy security. The economic and environmental performance of second-generation bioethanol production from corn stover in the European Union (EU) is studied, starting in Belgium as base case. A comparative environmental techno-economic assessment has been conducted, with process simulations in Aspen Plus and corn stover availability data in thirteen EU countries to calculate minimum ethanol selling prices (MESP) and Greenhouse gas emissions (GHGe). In this analysis, the emphasis is on the comparison of different pretreatment technologies, namely (i) dilute acid, (ii) alkaline, (iii) steam explosion and (iv) liquid hot water. Dilute acid showed the best economic and environmental performance for the base case scenario. Within the EU, Hungary and Romania presented the lowest MESP for the steam explosion model at 0.39 and 0.43 EUR/L respectively. Poland showed the lowest GHGe, at 0.46 kg CO2eq/L for the alkaline model, mainly due to the avoided product allocation on electricity and its high carbon intensity in the electricity generation sector. The second lowest GHGe were obtained in France for the dilute acid model and are attributed to its low agricultural emissions intensity. This study identifies a location-dependence of the economic and environmental performance of pretreatment technologies, which can be extrapolated from the EU to other large regions around the world and should be taken into consideration by decision-makers.