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Abstract Economically harvesting energy from a microbial fuel cell (MFC), increasing its electrical power production, and developing its role as a practical energy supply, needs a low-cost and high-performance design of the MFC compartments. According to this strategy, a novel monolithic membrane electrode assembly (MEA) was fabricated and evaluated as an air–cathode in a single-chamber MFC (SCMFC). The MEA was made of bacterial cellulose (BC), conductive multi-walled carbon nanotubes (CNT), and nano-zycosil (NZ). BC, as a nano-celluloses with oxygen barrier property, can maintain anaerobic conditions for the anode compartment. Binder-less CNT coating on BC avoids costly binders such as poly-tetra fluoro ethylene (PTFE) and Nafion and decreases the MEA charge transfer resistance. NZ, as a very cheap modifier, not only prevents the anolyte leakage but also provides more MEA’s active sites for the oxygen reduction reaction (ORR). The electrochemical performance of the MEA was compared to a PTFE- based gas diffusion electrode (GDE) in the SCMFC. The MEA cell provided a pulse power density of 1790 mW/m2, roughly twice as high as the pulse power density of GDE (920 mW/m2). SCMFC’s internal resistance decreased from 1.84 KΩ (with GDE) to 0.8 KΩ (with MEA). Also, the cell’s columbic efficiency increased from 4.2% (with GDE) to11.7% (with MEA). Additionally, the capacitance of the MEA (65 mF) was much higher than the value for GDE (0.73 mF). Thus, the MEA compared to the GDE showed higher performance in the SCMFC for electricity generation and wastewater treatment at a lower cost.

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.

Abstract Hybrid generation of large-scale photovoltaic (PV) power together with hydropower offers a promising option to promote the integration of PV power, because hydro units can complement variable PV generation rapidly at relatively low cost. However, the strong variations in PV generation create uncertainties for the operation of the hydro units. To improve guidelines for large-scale hydro–PV plant operation, a stochastic hydro unit commitment model considering the uncertainty in forecasting PV power is presented. This model seeks robust solutions (i.e., hydro unit status) to minimize the hydro plant’s water consumption when external load demands are imposed onto the hybrid system. A two-layer nested optimization framework is proposed to solve the model in a hierarchical structure. In the outer layer, a cuckoo search algorithm, combined with a novel encoding strategy, optimizes the number of online units that can meet the load demand under all PV generation processes. In the inner layer, load dispatch schemes for the given number of online units are determined by dynamic programming. China’s Longyangxia hydro–PV plant was selected as a case study. Operational results were compared for three scenarios: actual operation, deterministic operation without consideration of PV forecast errors, and stochastic operation with consideration of PV forecast errors. The results indicate that: (1) the encoding strategy can handle the minimum online and offline time constraints, and can decrease optimization dimensionality; (2) the two-layer nested optimizer can make robust and effective decisions within an acceptable period when using the pre-stored optimization results provided by dynamic programming; and (3) water consumption in deterministic and stochastic operation scenarios decreased by 1.5% and 1.0%, respectively, compared to that of actual operation. These findings verify the applicability and effectiveness of the proposed methods, and also highlight the importance of decreasing uncertainty in the PV power forecast.

In this study, KOH and ZnCl2 were impregnated into sewage sludge as activation agents to produce activated sludge char in the pyrolysis step for the De-NOx process. The emission characteristics of volatile combustion from the pyrolysis of raw sewage sludge (SS-Raw), sewage sludge spiked with KOH (SS-KOH), and sewage sludge spiked with ZnCl2 (SS-ZnCl2) were investigated. In addition, the De-NOx effects and the characteristics of the prepared chars, including specific surface areas, pore distributions, functional groups, were explored. The exploration results showed that the pollutants generated during the volatile combustion process could be divided into primary pollutants (SO2, NO, N2O, and HCl) and minor pollutants (CO, NH3, and HCN). Under the conditions of oxygen-rich combustion, SO2 and NOx emissions from SS-KOH were 0% and 113.2% of those from SS-Raw respectively. SS-ZnCl2 exhibited the similar SO2 and NOx emissions to those of SS-KOH. However, SS-ZnCl2 released considerable HCl during the pyrolysis process, thus limiting its application. Sludge char from SS-KOH (SC-KOH) also exhibited the best De-NOx performance compared to the chars from SS-Raw and SS-ZnCl2 and the De-NOx efficiency was 56% higher than that of sludge char from SS-Raw (SC-Raw). Therefore, with KOH-impregnated SS, activated sludge char can be produced via one pyrolysis step and used in the De-NOx process.

Abstract Green infrastructure (GI) is a low-carbon solution for urban rainwater management. Hydrological processes and the corresponding emissions of greenhouse gas (GHG) during rainfall events are optimized by GI when the latter is compared with a traditional urban drainage system. This study establishes an city-scale quantitative analysis, based on hydrological processes, with which to assess the contribution of GIs to low-carbon urban drainage systems and cities. The emission factor method is applied to measure GHG emissions. Attributable sources of emissions are wastewater treatment plants and wastewater and rainwater pumps. The amount and rate of change in GHG emissions were selected as indicators of the impacts of GI-based urban drainage systems and a case study was conducted in Dongying, China, based on 48 hydrological scenarios from 1970 to 2017. The amount of annual GHG emissions decreased by 3752.5 to 26238.9 tons of CO2 equivalent at an average of 10677.3 tons/a. The rate of annual GHG emissions decreased by 25.9–68.7% with an average reduction of 45.9%. An S-shaped logistic curve fit the data, indicated that annual rainfall is non-linearly and positively correlated with both the amount and rate of annual GHG emissions mitigated. The probability of benefits to GHG emissions in the 48 hydrological scenarios is analyzed based on a Pearson type III distribution curve. These findings can provide information that local authorities can use to guide policies towards their goals of applying GIs to mitigate GHG emissions in the urban drainage system.

The partitioning behaviours of CO2 with three kinds of common impurities, i.e., N2, CH4 and H2S, in the formation brine are investigated by numerical simulations. The results indicate that the effects of N2, CH4 or the mixture of N2 and CH4 at the same concentrations are generally similar. The leading gas front is usually made up of less soluble impurities, such as N2, CH4 or the mixture of N2 and CH4, while more soluble species such as H2S has dissolved preferentially in the formation brine. The separations between different gas species increase as the gas displacement front migrates forwards and contacts more of the aqueous phase. Compared with the partitioning results of the 98% CO2 and 2% H2S mixture, the results indicate that the inclusion of less soluble N2 and/or CH4 results in an earlier gas breakthrough and a longer delay between the breakthrough times of CO2 and H2S. The early breakthrough of the gas phase is mainly because that the addition of N2 and/or CH4 lowers the viscosity of the gas phase, resulting in a higher gas velocity than that of the CO2–H2S mixture. Meanwhile, the mobility ratio is higher and the gas mixture contacts the formation brine over a larger area, giving rise to more efficient stripping of the more soluble gas species like H2S and thus larger separations. In the meantime, with the same total concentrations of impurities (12%), when 2% H2S is contained in the CO2 streams, gas phase flows slower and thus the breakthrough time is later. Furthermore, the effects on the partitioning phenomenon are weaker with decreasing concentrations of N2 and/or CH4 (from 10% to 2%) with fixed concentrations of other impurity like H2S (2%). The migration distances and the separations between different gas species change linearly with time on the whole, as confirmed by a simulation in a longer model.

Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of "hydrothermal simultaneous transmission" (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit's output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater. Accepted by Applied Energy

Abstract Clathrate hydrate-based desalination (HyDesal) is a promising desalination technology but it is energy intensive. Developing strategies to reduce the high energy consumption of HyDesal process is necessary for its industrial application. The need for refrigeration requirement for the operation of HyDesal can be offset by LNG cold exergy to reduce its energy consumption. However, the LNG cold exergy utilization efficiency is low due to the large temperature difference between LNG and seawater and hydrate former. In this work, we propose a sustainable process that integrates HyDesal and organic Rankine cycle by utilizing LNG cold exergy to generate fresh water and electricity simultaneously. This integrated process was optimized by adopting particle swarm optimization algorithm to achieve maximal power and fresh water generation. Further, an economic analysis was performed to compare the economic performance of the proposed system and the base case. The results showed that the proposed process could achieve zero specific energy consumption for desalination and generate extra power. The largest fresh water production and power generation of 165.3 tonne/h and 3480 kW were achieved by adopting cyclopentane as hydrate former and mixed working fluid in organic Rankine cycle based on 100 tonne/h of LNG flowrate. The lowest levelized cost of water of the proposed process was 1.946 $ /m3, which was 21.05% lower than that of the base case. Thus, the proposed sustainable process can strengthen the energy–water nexus and reduce the greenhouse gas emission by utilizing LNG cold exergy.