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In recent years, the rapid development of the rare earth industry has had a serious impact on the environment. Some enterprises have taken measures to improve the production process. In order to explore the sustainability of this industry and these improvements’ environmental benefits, this paper combines emergy analysis and lifecycle assessment to evaluate and compare the production process of rare-earth oxides considering the three aspects of emergy flow, pollutant emissions, and emergy-based indicators. Changes in the emergy of pollutant emissions before and after improvement of the production process are discussed. The results show that the greatest inputs in the mining and beneficiation stage and smelting separation stage are labor force and service and non-renewable resources, respectively. These two production stages are highly dependent on external input and have weak competitiveness. Both stages place great pressure on the environment, so the bastnasite production process would be unsustainable in the long term. After the improvement, the environmental impact of the production process for bastnaesite changed significantly, indicating that the improvement effect of the wastewater treatment facilities and the change of fuel from coal to natural gas is remarkable.

Environment-friendly treatment of sewage sludge has become tremendously important. Conversion of sewage sludge into energy products by environment-friendly conversion process, with its energy recovery and environmental benefits, is being paid significant attention. Direct liquefaction of sewage sludge into bio-oils with supercritical water (SCW) was therefore put forward in this study, as de-water usually requiring intensive energy input is not necessary in this direct liquefaction. Supercritical water may act as a strong solvent and also a reactant, as well as catalyst promoting reaction process. Experiments were carried out in a self designed high-pressure reaction system with varying operating conditions. Through orthogonal experiments, it was found that temperature and residence time dominated on bio-oil yield compared with other operating parameters. Temperature from 350 to 500°C and reaction residence time of 0, 30, 60min were accordingly investigated in details, respectively. Under supercritical conversion, the maximum bio-oil yield could achieve 39.73%, which was performed at 375°C and 0min reaction residence time. Meanwhile, function of supercritical water was concluded. Fuel property analysis showed the potential of bio-oil application as crude fuel.

Abstract A great deal of heat is wasted in intensive public shower facilities, such as those in schools, barracks and natatoriums, which open up at specified time. It will contribute a lot to energy saving and environmental protection with significant economic benefits to recycle the exhaust heat. In this paper, we propose two different kinds of heat pumps (an electric heat pump and an absorption heat pump) in the heat recovery systems. In both system, the used shower water is drained through a pipe and collected in a gray water pool. When the wastewater reaches certain volume, the heat pump system will begin working and recycling heat. The wastewater is filtered and piped to the heat exchanger to exchange heat with the tap water whose temperature will increase from 12 °C to 25 °C with the wastewater temperature dropping from 30 °C to 17 °C. Then the wastewater is piped to the heat pump evaporator and the tap water is piped to the condenser for farther heating. According to the different characteristics of the electric heat pump and absorption heat pump, we also introduce the processes and control methods of different heat recovery systems in details in this paper. Based on a practical example, this paper analyzes and compares the economic and environmental benefits of three retrofitting schemes, including “exhaust heat recovery using electric heat pump”, “exhaust heat recovery using electric heat pump + gas boiler” and “exhaust heat recovery using direct-fired heat pump”. Then we find out that the heat recovery system using direct-fired absorption heat pump has lower energy consumption, less pollution, lower operating cost, and shorter payback period. And it has a promising practical application.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

To find an appropriate method for sulfate-rich wastewater containing ethanol and acetate with COD/sulfate ratio of 1, a UASB reactor was operated for more than 180 days. The influences of HRT (hydraulic retention time) and OLR (organic loading rate) on organics and sulfate removal, gas production, and electrons utilization were investigated. The sludge activity and microorganism composition were also determined. The results indicated that this system removed more than 80% of COD and 30% of sulfate with HRT above 6h and OLR below 12.3 gCOD/L d. Further HRT decrease caused volatile fatty acids accumulation and performance deterioration. Except at HRT of 2h, COD and electron flow were mostly utilized by methane-producing archaea (MPA), and methane yield remained in the range of 0.18-0.24 LCH4/gCOD. Methane was mainly generated by Methanosaeta concilii GP6 with acetate as substrate, whereas sulfate was mainly reduced by incomplete-oxidizing Desulfovibrio species with ethanol as substrate.

Abstract The 2013/2017 Nankai Trough (Japan) and 2017 Shenhu Area (China) offshore methane hydrate production tests showed the world the possibility and feasibility of the oceanic methane hydrate production by depressurization. However, the relatively low gas production rate still remained as one of the critical bottlenecks for the economical utilization. This study chose the Nankai Trough as a target area, and aimed at the gas recovery enhancement from the methane hydrate reservoir using vertical wells. A traditional single-vertical-well system and a new dual-vertical-well system were proposed, and special production strategies of the aggressive depressurization and permeability improvement were applied to these two systems for the effectiveness verification. Based on the 15-year simulation results, it was found that the middle low-permeability silt-dominated layers in the reservoir held the key to the gas recovery enhancement, and for the single-vertical-well system, the permeability improvement in this sublayer seemed more reliable and feasible than the aggressive depressurization. On the other hand, the dual-vertical-well system significantly exceeded the single-vertical-well system due to the synergistic effect of the two wellbores, and could raise the average gas production rate (9.5 × 103 m3/day) by one order of magnitude (to 7.9 × 104 m3/day). Moreover, if this new system was combined with the aggressive depressurization, the average gas production rate could be further raised by one order of magnitude (to 3.4 × 105 m3/day).

In order to enhance the efficiency and benefits of the sludge anaerobic digestion process, K2FeO4 was added to a sludge anaerobic digestion system, and its effects on the system were comprehensively investigated. Results showed that sludge anaerobic digestion was greatly improved by adding 500 mg/L K2FeO4. Biogas and methane productions were increased by 26.6 and 28.4%, respectively. Sludge reduction, protein removal, and the conversion efficiency of dissolved organics were all improved. The mechanism revealed that the disintegration of sludge flocs, enhancement of protease activity, and decrease of soluble sulfide toxicity on microorganisms contributed to biogas production and sludge reduction. Biogas quality was improved, benefitting from the decreasing H2S content in biogas; as additionally, the cost of biogas desulfuration was reduced. In the biogas slurry treatment, the PO43--P concentrations were decreased by 39%, which also reduced the cost of the dephosphorization processes at certain extent.

The U.S. Environmental Protection Agency’s (EPA) Superfund—the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) database—has collected and built an open-source database based on nearly 2000 US soil remediation cases since 1980, providing detailed information and references for researchers worldwide to carry out remediation work. However, the cases were relatively independent to each other, so the whole database lacks systematicness and instructiveness to some extent. In this study, the basic features of all 144 soil remediation projects in four major oil-producing states (California, Texas, Oklahoma and Alaska) were extracted from the CERCLA database and the correlations among the pollutant species, pollutant site characteristics and selection of remediation methods were analyzed using traditional and machine learning techniques. The Decision Tree Classifier was selected as the machine learning model. The results showed that the growth of new contaminated sites has slowed down in recent years; physical remediation was the most commonly used method, and the probability of its application is more than 80%. The presence of benzene, toluene, ethylbenzene and xylene (BTEX) substances and the geographical location of the site were the two most influential factors in the choice of remediation method for a specific site; the maximum weights of these two features reaches 0.304 and 0.288.