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Using abandoned gas wells as geothermal resources for energy production is an effective way to extract geothermal energy from geological formations. These abandoned wells have the potential to significantly contribute in the rising global demand for energy without requiring the land disruption resulting from deep drilling or digging, processes necessary for energy extraction from geological formations via more traditional methods. In this paper, a method to extract geothermal energy from abandoned gas wells is proposed. The method offers an efficient, economical, and environmentally-conscious way to generate electricity. A mathematical model of a thermal and hydraulic coupling process is constructed, and a 3D numerical model is generated to study the process of geothermal energy extraction by retrofitting an abandoned gas reservoir into a geothermal reservoir. Using the model, heat extraction and fluid flow are analyzed over a period of 50 years. The heat production, electricity generation, and thermal recovery over the lifetime of the reservoir indicate that a commercially viable geothermal dual well system can produce geothermal energy effectively. Dual-well systems contain at least one injection well and one production well. They are composed of a two-way flow system in which the fluid flows into the reservoir via an injection well and returns from the production well having absorbed thermal energy from the surrounding rocks. Sensitivity analysis of the main parameters controlling the average outlet temperature of the fluid from the sedimentary geothermal system reveals that abandoned gas wells are a suitable source of geothermal energy. This energy can be harvested via a method whose use of reservoir fluids differs from that of the traditional method of closed-loop circulation via a borehole heat exchanger. Here, it is demonstrated that abandoned oil and gas fields can be repurposed to be geothermal energy sources that provide low-cost electricity and are economically sustainable. [ABSTRACT FROM AUTHOR]

The Sustainable Energy Development Authority of Malaysia (SEDA) regularly receives complaints about damaged components and distribution boards of PV systems due to lightning strikes. Permanent and momentary interruptions of distribution circuits may also occur from the disturbance. In this paper, a solar PV Rooftop system (3.91 kWp) provided by SEDA was modelled in the PSCAD/EMTDC. The Heidler function was used as a lightning current waveform model to analyse the transient current and voltage at two different points susceptible to the influence of lightning events such as different lightning current wave shape, standard lightning current and non-standard lightning current. This study examines the effect on the system components when lightning directly strikes at two different points of the installation. The two points lie between the inverter and the solar PV array and between inverter and grid. Exceptionally high current and voltage due to the direct lightning strike on a certain point of a PV Rooftop system was also studied. The result of this case study is observed with and without the inclusion of surge protective devices (SPDs). The parameters used were 31 kA of peak current, 10 metres cable length and lightning impulse current wave shape of 8/20μs. The high current and voltage at P1 striking point were 31 kA and 2397 kV, respectively. As for the AC part, the current and voltage values were found to be 5.97 kA and 5392 kV, respectively.Therefore, SPDs with suitable rating provided by SEDA were deployed. Results showed that high transient current voltage is expected to clamp sharply at the values of 1.915 kV and 0 A at the P1 striking point. As for the AC part, the current and voltage values were found to be 0 kA and 0.751 V, respectively. Varying lightning impulse current wave shapes at striking point P2 showed that the highest voltage was obtained at waveshape 10/350 μs at 11277 kV followed by wave shapes of 2/70 μs, 8/20 μs and 0.7/6 μs. The high value of transient voltage was clamped at a lower level of 2.029 kV. Different lightning amplitudes were also applied, ranging from 2–200 kA selected based on the CIGRE distribution. It showed that the current and voltage at P1 and P2 were directly proportional. Therefore, the SPD will be designed at an acceptable rating and proper position of SPD installation at solar PV Rooftop will be proposed. The results obtained in this study can then be utilised to appropriately assign a SPD to protect the PV systems that are connected to the grid. Installing SPDs without considering the needs of lightning protection zones would expose the expensive equipment to potential damage even though the proper energy coordination of SPDs is in place. As such, the simulation results provide a basis for controlling the impacts of direct lightning strikes on electrical equipment and power grids and thus justify SPD coordination to ensure the reliability of the system. [ABSTRACT FROM AUTHOR]

This paper develops two-stage inverse data envelopment analysis models with undesirable outputs to formulate resource plans for 16 Chinese listed commercial banks whose outputs are increased and overall efficiency is kept unchanged in the short term. We use these models to meet three different output targets, namely, increasing both the desirable and undesirable outputs by the same percentage, increasing these outputs by different percentages, and increasing only the desirable outputs while keeping the undesirable outputs unchanged. We find that operation cost and interest expense are more flexible than labor in the adjustment process and that deposits have no obvious law of change. The findings of this work provide some suggestions for bank managers. [ABSTRACT FROM AUTHOR]

Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-based (MLP, GRNN and ANFIS), kernel-based (SVM, KNEA), tree-based (M5Tree, XGBoost) and curve-based (MARS) models, for predicting daily ET0 with maximum/maximum temperature and precipitation data during 2001–2015 from 14 stations in various climatic regions of China, i.e., arid desert of northwest China (NWC), semi-arid steppe of Inner Mongolia (IM), Qinghai-Tibetan Plateau (QTP), (semi-)humid cold-temperate northeast China (NEC), semi-humid warm-temperate north China (NC), humid subtropical central China (CC) and humid tropical south China (SC). The results showed machine learning models using only temperature data obtained satisfactory daily ET0 estimates (on average R2 = 0.829, RMSE = 0.718 mm day−1, NRMSE = 0.250 and MAE = 0.508 mm day−1). The prediction accuracy was improved by 7.6% across China when information of precipitation was further considered, particularly in (sub)tropical humid regions (by 9.7% in CC and 12.4% in SC). The kernel-based SVM, KNEA and curve-based MARS models generally outperformed the others in terms of prediction accuracy, with the best performance by KNEA in NWC and IM, by SVM in QTP, CC and SC, and very similar performance by them in NEC and NC. SVM (1.9%), MLP (2.0%), MARS (2.6%) and KNEA (6.4%) showed relatively small average increases in RMSE during testing compared with training RMSE. SVM is highly recommended for predicting daily ET0 across China in light of best accuracy and stability, while KNEA and MARS are also promising powerful models. [ABSTRACT FROM AUTHOR]

We test and compare different incentives in motivating water conservation using a randomized controlled trial. In a field experiment carried out with Singaporean households, regular feedback was given, with informative, normative and monetary incentives provided to different groups. Evidence shows that all households saved an average of 4 Litres of water per person per day, with no difference in treatment effect found across various groups. Perhaps unsurprisingly, the water saving effect is also found to be more significant with high baseline users, who saved up to 5.9 Litres per person per day. High baseline households also respond more positively to the non-monetary incentives. [ABSTRACT FROM AUTHOR]

Background: Japanese encephalitis virus (JEV) is a major cause of encephalitis in Asia, and the commonest cause of mosquito-borne encephalitis worldwide. Detection of JEV RNA remains challenging due to the characteristic brief and low viraemia, with 0–25% of patients positive, and the mainstay of diagnosis remains detection of anti-JEV IgM antibody. Methods: We performed a systematic review of published RT-PCR protocols, and evaluated them in silico and in vitro alongside new primers and probes designed using a multiple genome alignment of all JEV strains >9,000nt from GenBank, downloaded from the NCBI website (November 2016). The new assays included pan-genotype and genotype specific assays targeting genotypes 1 and 3. Results: Ten RT-qPCR assays were compared, a pre-existing in-house assay, three published assays and six newly designed assays, using serial RNA dilutions. We selected three assays, one published and two novel assays, with the lowest limit of detection (LOD) for further optimisation and validation. One of the novel assays, detecting NS2A, showed the best results, with LOD approximately 4 copies/ reaction, and no cross-reaction on testing closely related viruses in the JEV serocomplex, West Nile Virus and St. Louis Virus. The optimised assays were validated in consecutive patients with central nervous system infections admitted to hospitals in Laos, testing paired CSF and serum samples. Conclusions: We succeeded in developing a JEV specific RT-qPCR assay with at least 1 log10 improved sensitivity as compared to existing assays. Further evaluation is required, field-testing the assay in a larger group of patients. [ABSTRACT FROM AUTHOR]