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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.

Heavy Metals Content Pb, Cd, Cu, Zn And Ni In Sea Water And Sediment In Membramo Estuary And Its Relationship With Fishery Cultivation. Obervation on heavy metals Pb, Cd, Cu, Zn and Ni content in Jakarta Bay were carried out in August 2003. The results showed that the Pb, Cd, Cu, Zn, and Ni content still in line with threshold value stated by for fisheries. By the all, in sea water Zn content is higher compared to the others, while in sediment Ni is higher. This data showed the result show that on waters of Membramo River Zn and Ni waste than others elements. Keywords: Membramo, heavy metals, fisheries.

In this paper, we propose and investigate an original approach to energy conversion based on polyzwitterionic hydrogels, which exhibit an antipolyelectrolyte effect that enables them to swell in salt water and shrink in water of a different (i.e., desalinated water) salinity. The swelling and shrinking processes run cyclically and can move a piston up or down reversibly, thus transforming the antipolyelectrolyte effect into a mechanical force based on the salinity gradient. This phenomenon makes polyzwitterionic hydrogels suitable for use in a smart, polymeric engine. We apply this approach to investigate energy recovery from a polysulfobetaine-based hydrogel. The cross-linking density, external load, particle size, and repeatability of energy recoverability of hydrogels are examined. The maximum energy recovery from 0.4 g of hydrogel in feed (calculated based on dry form) of 102 mJ/kg was obtained by a hydrogel with a 3% cross-linking density, a 200-300 μm particle size, and 100 g external load. Excellent reproducibility of engine cycles was achieved over 10 cycles. This concept is complementary to the osmotic engine concept based on a polyelectrolyte hydrogel. In addition, polyzwitterionic materials have become a benchmark material for preventing biofouling, and the swelling properties of such materials can be further modulated and tuned.

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.

Sustainability of agro-food supply chains has recently become the subject of greater interest from consumers, firms, governmental organizations and academia as the environment continues to deteriorate. One of the most critical factors influencing the sustainability of an agro-food supply chain is its network design. A particularly challenging aspect in this context is the broad range of influencing indicators associated with the Triple Bottom Line (TBL) of sustainability that need to be considered. However, many of these indicators could not be fully integrated or measured by single-step optimization problems. This paper presents a critical literature review of operational research methods for the design of sustainable supply chains. A novel two-stage hybrid solution methodology is proposed. In the first stage, a partner selection is performed using a hybrid multi criteria decision making based on Analytic Hierarchy Process (AHP) method and the Ordered Weighted Averaging (OWA) aggregation method. The result obtained in the first stage is used in the second stage to develop a multi-objective mathematical model to optimize the design of the supply chain network. This approach allows the simultaneous consideration of all three dimensions of sustainability including carbon footprint, water footprint, number of jobs created and the total cost of the supply chain design. The proposed approach generates a Pareto frontier to aid users in making decisions. Numerical experiments are completed utilizing data from an agro-food company to demonstrate the efficiency and effectiveness of the proposed solution methodology. The analyzes of the numerical results provide important organizational, practical and policy insights on (1) the impact of financial and environmental sustainability on supply chain network design (2) the tradeoff analysis between environmental emission, water footprint, societal implications and associated cost for making informed decision on supply chain investment.

AbstractThe Controlled Freeze Zone™ technology removes CO2 and H2S from natural gas in a single step cryogenic distillation process. Removal and management of acid gas impurities from natural gas pose significant challenges in developing sour gas fields. In many cases CFZ™ is capable of processing sour gases with a wide range of CO2 and H2S compositions at a lower cost than conventional technologies. The acidic components are removed as a high pressure liquid that can be injected into reservoirs for geosequestration or, when of suitable composition, to improve oil recovery. In either case, sulfur production from H2S and release of CO2 to the atmosphere can be eliminated.CFZ™ technology was successfully demonstrated through earlier pilot plant operations. Currently, ExxonMobil Upstream Research Company is advancing CFZ™ to large scale commercial readiness through a commercial demonstration plant in Wyoming, USA. By building the commercial demonstration plant at ExxonMobil’s world-class Shute Creek gas treating and acid gas injection facility, integration of CFZ™ with acid gas injection, will also be demonstrated when the unit is operated in 2010–2011.

Abstract The impact of CO 2 leakage from underground storage formations on shallow water resources is a concerning aspect in CO 2 capture and storage (CCS) risk assessment. In Campo de Calatrava region (Spain), natural CO 2 fluxes from the Earth’s mantle interact with shallow aquifers, resulting in significant changes in their physical and chemical properties. The resultant water is slightly acidic (pH 5.9-6.4), oxidizing, and enriched in iron (up to 6.1×10 -4 mol·L -1 ) and other metals usually found at trace concentrations. Thermodynamic calculations reveal that aqueous Fe(III) carbonate complexes play an important role in the persistence of this high concentration of iron and trace metals in solution.