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Abstract Small Mediterranean islands are remote, off-grid communities characterized by carbon intensive electricity systems coupled with high energy consuming desalination technologies to produce potable water. The aim of this study is to propose a novel dynamic, multi-objective optimization approach for improving the sustainability of small islands through the introduction of renewable energy sources. The main contributions of our approach include: (i) dynamic modelling of desalination plant operations, (ii) joint optimization of system design and operations, (iii) multi-objective optimization to explore trade-offs between potentially conflicting objectives. We test our approach on the real case study of the Italian Ustica island by means of a comparative analysis with a traditional non-dynamic, least cost optimization approach. Numerical results show the effectiveness of our approach in identifying optimal system configurations, which outperform the traditional design with respect to different sustainability indicators, limiting the structural interventions, the investment costs and the environmental impacts. In particular, the optimal dynamic solutions able to satisfy the whole water demand allow high levels of penetration of renewable energy sources (up to more than 40%) to be reached, reducing the net present cost by about 2–3 M€ and the CO2 emissions by more than 200 tons/y.

This paper investigates thermal mixing caused by the inflow from one or two round, horizontal, buoyant jets in a water storage tank, which is part of a thermal solar installation. A set of experiments was carried out in a rectangular tank with a capacity of 0.3 m3, with one or two constant temperature inflows. As a result, two correlations based on temperature measurements have been developed. One of the correlations predicts the size of a zone of homogenous temperature, referred to herein as the mixing zone, which develops when a single hot inflow impinges on the opposite wall of the tank. The other identifies the degree of mixing resulting from the interaction between a hot inflow and a cold inflow located below the hot one. The correlations are combined with energy balances to predict the amount of hot water available in a tank with open side inlets and the corresponding temperatures of the outflows. Outdoor measurements were also performed in a solar installation, in which a commercial water storage tank with a 1.5 m3 capacity, heated by a solar collector array with a useful surface area of 42.2 m2, drives a LiBr–H2O absorption chiller. Comparison of the predicted and measured outflow temperatures under a variety of weather conditions shows a maximum difference of 3 °C.

Abstract Global demand for methanol as both a chemical precursor and a fuel additive is rising. At the same time, numerous renewable methanol production pathways are under development, which, if commercialized, could provide significant environmental benefits over traditional methanol synthesis pathways. However, it is difficult to compare technologies at different maturity levels, with differing feedstocks, and with significant differences in overall process design. Thus, there is a need to harmonize the analyses of renewable pathways using a consistent techno-economic approach to evaluate the potential for commercialization of various pathways. This analysis uses a novel cross-comparison method to assess near-term and long-term viability of both low- and high-maturity level technologies. The techno-economic assessment considers cost factors critical to market acceptance combined with carbon- and energy-efficiency assessments of three renewable pathways compared with a commercial baseline. We find that biomass gasification to methanol represents a near-term viable pathway with a high technology readiness level and commercially competitive market price. If cost-reducing technological improvements can be realized and scaled up in the CO2 electrolysis pathways, the potential for higher carbon efficiencies may help drive market adoption of these more modular, direct conversion pathways in future markets as they present an opportunity to better support global decarbonization efforts through efficient waste carbon utilization.

Fossil fuel based power generation is and will still be the back bone of our world economy, albeit such form of power generation significantly contributes to global CO2 emissions. Solar energy is a clean, environmental friendly energy source for power generation, however solar photovoltaic electricity generation is not practical for large commercial scales due to its cost and high-tech nature. Solar thermal is another way to use solar energy to generate power. Many attempts to establish solar (solo) thermal power stations have been practiced all over the world. Although there are some advantages in solo solar thermal power systems, the efficiencies and costs of these systems are not so attractive. Alternately by modifying, if possible, the existing coal-fired power stations to generate green sustainable power, a much more efficient means of power generation can be reached. This paper presents the concept of solar aided power generation in conventional coal-fired power stations, i.e., integrating solar (thermal) energy into conventional fossil fuelled power generation cycles (termed as solar aided thermal power). The solar aided power generation (SAPG) concept has technically been derived to use the strong points of the two technologies (traditional regenerative Rankine cycle with relatively higher efficiency and solar heating at relatively low temperature range). The SAPG does not only contribute to increase the efficiencies of the conventional power station and reduce its emission of the greenhouse gases, but also provides a better way to use solar heat to generate the power. This paper presents the advantages of the SAPG at conceptual level.

Abstract This work reports the use of mid- and near-infrared spectroscopy (MIR and NIR) to predict the kinematic and dynamic viscosities of biodiesel–diesel blends. A partial least square regression (PLSR) modeling method was employed to develop the calibration models based on information from four commonly used biodiesel and three different commercial diesel fuels. For MIR spectroscopy, wavelengths in the fingerprint region of 550–1500 cm−1 were chosen for developing the model. The root mean square error of prediction (RMSEP) for kinematic viscosity and dynamic viscosity were 0.114 and 0.119 mm2/s, respectively, based on the validation set that consisted of 26 biodiesel–diesel blend samples made of six different biodiesel and three different diesel fuels. For the NIR spectroscopy, the PLSR model established using the spectral regions of 1100–1500 nm, 1600–1700 nm, and 1800–2200 nm obtained better results. The RMSEP were 0.070 mm2/s for kinematic viscosity and 0.062 mm2/s for dynamic viscosity prediction. The results indicated that both MIR and NIR can be used to accurately predict the viscosities of biodiesel–diesel blends, but better results can be obtained using NIR spectroscopy.

Abstract Uninterrupted energy harvest is critical for self-sustained wastewater monitoring in order to achieve efficient and resilient operation of decentralized onsite wastewater treatment facilities. To address this long-standing challenge, an integrated power entity consisting of a miniature microbial fuel cell (volume: 1.5 mL) and a triggered power management system was developed in this study to power the potentiometric millimeter-sized solid-state water sensors for real-time in situ monitoring and uninterrupted transmission of sensor readings (indicating ammonium concentration) under both ammonium shock and toxic shock in wastewater. Specifically, a data trigger including two capacitors, an operation amplifier and a low-power comparator is equipped in the power management system as a switch for turning on power discharge for data transmission once the ammonium shock is captured by the potentiometric sensors, enabling a sufficient recharge duration to store the power needed for high frequency data transmission (16.23 times/min) required under shocks. Furthermore, this power-sensor entity possesses a unique dual-screening capability of capturing the ammonium and toxic shocks, providing an early warning for swift decision making, reducing ~17% of ammonium discharge and saving ~42% of energy consumption in decentralized onsite wastewater treatment facilities.

Abstract Carbon capture and storage (CCS) technology is an important option in the portfolio of emission mitigation solutions in scenarios that lead to deep reductions in greenhouse gas (GHG) emissions. We focus on CCS application in hard-to-abate sectors (cement industry, iron and steel, chemicals) and introduce industrial CCS options into the MIT Economic Projection and Policy Analysis (EPPA) model, a global multi-region multi-sector energy-economic model that provides a basis for the analysis of long-term energy deployment. We use the EPPA model to explore the potential for industrial CCS in different parts of the world, under the assumptions that CCS is the only mitigation option for deep GHG emission reductions in industry and that negative emission options are not available for other sectors of the economy. We evaluate CCS deployment in a scenario that limits the increase in average global surface temperature to 2 °C above preindustrial levels. When industrial CCS is not available, global costs of reaching the target are higher by 12% in 2075 and 71% in 2100 relative to the cost of achieving the policy with CCS. Overall, industrial CCS enables continued growth in the use of energy-intensive goods along with large reductions in global and sectoral emissions. We find that in scenarios with stringent climate policy, CCS in the industry sector is a key mitigation option, and our approach provides a path to projecting the deployment of industrial CCS across industries and regions.

Abstract The distributed generation (DG) of combined heat and power (CHP) for commercial buildings is gaining increased interest, yet real-world installations remain limited. This lack of implementation is due, in part, to the challenging economics associated with volatile utility pricing and potentially high system capital costs. Energy technology application analyses are also faced with insufficient knowledge regarding how to appropriately design (i.e., configure and size) and dispatch (i.e., operate) an integrated CHP system. Existing research efforts to determine a minimum-cost-system design and dispatch do not consider many dynamic performance characteristics of generation and storage technologies. Consequently, we present a mixed-integer nonlinear programming (MINLP) model that prescribes a globally minimum cost system design and dispatch, and that includes off-design hardware performance characteristics for CHP and energy storage that are simplified or not considered in other models. Specifically, we model the maximum turn-down, start up, ramping, and part-load efficiency of power generation technologies, and the time-varying temperature of thermal storage technologies. The consideration of these characteristics can be important in applications for which system capacity, building demand, and/or utility guidelines dictate that the dispatch schedule of the devices varies over time. We demonstrate the impact of neglecting system dynamics by comparing the solution prescribed by a simpler, linear model with that of our MINLP for a case study consisting of a large hotel, located in southern Wisconsin, retrofitted with solid-oxide fuel cells (SOFCs) and a hot water storage tank. The simpler model overestimates the SOFC operational costs and, consequently, underestimates the optimal SOFC capacity by 15%.