Emerging electrolytes with fluorinated solvents for rechargeable lithium-based batteries.

Electrolytes that can ensure the movement of ions and regulate interfacial chemistries for fast mass and charge transfer are essential in many types of electrochemical energy storage devices. However, in the emerging energy-dense lithium-based batteries, the uncontrollable side-reactions and consumption of the electrolyte result in poor electrochemical performances and severe safety concerns. In this case, fluorination has been demonstrated to be one of the most effective strategies to overcome the above-mentioned issues without significantly contributing to engineering and technical difficulties. Herein, we present a comprehensive overview of the fluorinated solvents that can be employed in lithium-based batteries. Firstly, the basic parameters that dictate the properties of solvents/electrolytes are elaborated, including physical properties, solvation structure, interface chemistry, and safety. Specifically, we focus on the advances and scientific challenges associated with different solvents and the enhancement in their performance after fluorination. Secondly, we discuss the synthetic methods for new fluorinated solvents and their reaction mechanisms in depth. Thirdly, the progress, structure-performance relationship, and applications of fluorinated solvents are reviewed. Subsequently, we provide suggestions on the solvent selection for different battery chemistries. Finally, the existing challenges and further efforts on fluorinated solvents are summarized. The combination of advanced synthesis and characterization approaches with the assistance of machine learning will enable the design of new fluorinated solvents for advanced lithium-based batteries.

Asymmetric Solvents Regulated Crystallization-Limited Electrolytes for All-Climate Lithium Metal Batteries.

Electrolytes that can keep liquid state are one of the most important physical metrics to ensure the ions transfer with stable operation of rechargeable lithium-based batteries at a wide temperature window. It is generally accepted that strong polar solvents with high melting points favor the safe operation of batteries above room temperatures but are susceptible to crystallization at low temperatures (≤-40 °C). Here, a crystallization limitation strategy was proposed to handle this issue. We demonstrate that, although the high melting points of ethylene sulfite (ES, -17 °C) and fluoroethylene carbonate (FEC, ≈23 °C), their mixtures can avoid crystallization at low temperatures, which can be attributed to low intermolecular interactions and altered molecular motion dynamics. A suitable ES/FEC ratio (10 % FEC) can balance the bulk and interface transport of ions, enabling LiNi0.8 Mn0.1 Co0.1 O2 ||lithium (NCM811||Li) full cells to deliver excellent temperature resilience and cycling stability over a wide temperature range from -50 °C to +70 °C. More than 66 % of the capacity retention was achieved at -50 °C compared to room temperature. The NCM811||Li pouch cells exhibit high cycling stability under realistic conditions (electrolyte weight to cathode capacity ratio (E/C)≤3.5 g Ah-1 , negative to positive electrode capacity ratio (N/P)≤1.09) at different temperatures.

Directing (110) Oriented Lithium Deposition through High-flux Solid Electrolyte Interphase for Dendrite-free Lithium Metal Batteries.

Controlling lithium (Li) electrocrystallization with preferred orientation is a promising strategy to realize highly reversible Li metal batteries (LMBs) but lack of facile regulation methods. Herein, we report a high-flux solid electrolyte interphase (SEI) strategy to direct (110) preferred Li deposition even on (200)-orientated Li substrate. Bravais rule and Curie-Wulff principle are expanded in Li electrocrystallization process to decouple the relationship between SEI engineering and preferred crystal orientation. Multi-spectroscopic techniques combined with dynamics analysis reveal that the high-flux CF3 Si(CH3 )3 (F3 ) induced SEI (F3 -SEI) with high LiF and -Si(CH3 )3 contents can ingeniously accelerate Li+ transport dynamics and ensure the sufficient Li+ concentration below SEI to direct Li (110) orientation. The induced Li (110) can in turn further promote the surface migration of Li atoms to avoid tip aggregation, resulting in a planar, dendrite-free morphology of Li. As a result, our F3 -SEI enables ultra-long stability of Li||Li symmetrical cells for more than 336 days. Furthermore, F3 -SEI modified Li can significantly enhance the cycle life of Li||LiFePO4 and Li||NCM811 coin and pouch full cells in practical conditions. Our crystallographic strategy for Li dendrite suppression paves a path to achieve reliable LMBs and may provide guidance for the preferred orientation of other metal crystals.

Effects of conventional and heated tobacco product smoking on discoloration of artificial denture teeth.

STATEMENT OF PROBLEM: Cigarette smoke can cause discoloration of artificial denture teeth. However, studies on the effects of heated tobacco product smoke on artificial denture teeth are lacking. PURPOSE: The purpose of this in vitro study was to evaluate the effects of conventional cigarette and heated tobacco product smoke on the color stability of artificial denture teeth. MATERIAL AND METHODS: Ninety maxillary central incisor denture teeth (Endura Anterior HC5 A3; Shofu) were randomly divided into 3 groups (n=30). Teeth in the control group were exposed to air; those in group CC were exposed to conventional cigarette (Marlboro Medium; Philip Morris) smoke, and those in group HT were exposed to heated tobacco product (IQOS 2.4 plus holder, Marlboro Heets Silver; Philip Morris) smoke. Before the experiment, the shade of the artificial denture teeth was evaluated in accordance with the Commission International de I'Eclairage (CIELab) color system by using a spectrophotometer (Shadepilot; DeguDent GmbH). The average CIELab value was estimated by scanning the entire labial surface of each specimen. To simulate smoking, standard conditions described by the Coresta Recommended Method N°22 were used-the puff duration was 2 seconds, with a 60-second interval between puffs. For each cigarette, 6 puffs and 6 intervals were simulated across 372 seconds. A total of 105 cigarettes were used based on a smoking simulation of 15 cigarettes each day for 7 days. The teeth in the control group were stored in fresh air in the smoke chamber for the same period as those in the experimental groups. After the experiment, L∗, a∗, and b∗ values were measured, and ΔE was calculated to evaluate the color change. All statistical analyses were performed with a statistical software program using a paired t test to determine discoloration after exposure to cigarette smoke. One-way ANOVA and the Tukey test were used to evaluate the significant differences between groups (α=.05). RESULTS: Lightness was significantly lower in the CC and HT groups (P<.001). All CIELab values showed statistically significant differences in the CC group. The greatest color change was observed in the CC group (ΔE=6.93 ±0.59), whereas the HT group showed a clinically imperceptible color change (ΔE=0.79 ±0.21). Discoloration was minimal in the CC group (ΔE=0.34 ±0.13). CONCLUSIONS: Conventional cigarette and heated tobacco product smoke can change the color of denture teeth. Heated tobacco product smoke causes less discoloration of denture teeth.

Iron Selenide Microcapsules as Universal Conversion-Typed Anodes for Alkali Metal-Ion Batteries.

Rechargeable alkali metal-ion batteries (AMIBs) are receiving significant attention owing to their high energy density and low weight. The performance of AMIBs is highly dependent on the electrode materials. It is, therefore, quite crucial to explore suitable electrode materials that can fulfil the future requirements of AMIBs. Herein, a hierarchical hybrid yolk-shell structure of carbon-coated iron selenide microcapsules (FeSe2 @C-3 MCs) is prepared via facile hydrothermal reaction, carbon-coating, HCl solution etching, and then selenization treatment. When used as the conversion-typed anode materials (CTAMs) for AMIBs, the yolk-shell FeSe2 @C-3 MCs show advantages. First, the interconnected external carbon shell improves the mechanical strength of electrodes and accelerates ionic migration and electron transmission. Second, the internal electroactive FeSe2 nanoparticles effectively decrease the extent of volume expansion and avoid pulverization when compared with micro-sized solid FeSe2 . Third, the yolk-shell structure provides sufficient inner void to ensure electrolyte infiltration and mobilize the surface and near-surface reactions of electroactive FeSe2 with alkali metal ions. Consequently, the designed yolk-shell FeSe2 @C-3 MCs demonstrate enhanced electrochemical performance in lithium-ion batteries, sodium-ion batteries, and potassium-ion batteries with high specific capacities, long cyclic stability, and outstanding rate capability, presenting potential application as universal anodes for AMIBs.

A probabilistic modeling framework for assessing the impacts of large reservoirs on river thermal regimes - A case of the Yangtze River.

Hydrological and thermal river regimes have changed greatly due to the construction of reservoirs and dams. Water temperature changes have important significance for aquatic habitats and freshwater ecosystems. To investigate the impact of large reservoirs on the water temperature regime along the middle reach of the Yangtze River, we present a probabilistic modeling framework to ascertain the joint dependence structures of air-water temperature and discharge-water temperature between pre-reservoir and post-reservoir periods based on the copula theory. The results show that the principle of maximum entropy (POME) method can better estimate the marginal distributions of temperature regimes. Reservoir operation disturbed the dependence structures of air-water temperature, especially after the Three Gorges Reservoir (TGR) was put into operation. Different shifts in the occurrence probabilities of high or low water temperatures at the downstream and upstream stations under extreme air temperature and discharge are observed, indicating the great effects of reservoirs on the dependence structures of the downstream river flow and thermal regime. Relying on the developed model, we propose the appropriate ranges of air temperature and discharge to maintain a suitable water temperature for Chinese sturgeon (Acipenser sinensis) spawning activity. The results of this study demonstrate the influence of dams on the thermal regime and can be helpful for optimizing reservoir operations to enhance biological conservation in the Yangtze River.

Vine copula selection using mutual information for hydrological dependence modeling.

Hydrological risk analysis and management entails multivariate modeling which requires modeling the structure of dependence among different variables. Vine copulas have been increasing applied in multivariate modeling wherein the selection of vine copula structure plays a critical role. Inspired by the relationship between Mutual information (MI) and copula entropy (CE), this study discussed the connection between conditional mutual information (CMI) and CE and developed a mutual information-based sequential approach to select a vine structure which was based on original observations, and model-independent. Then, to reduce the complexity of R-vine copulas, a statistical method-based truncation procedure was applied. Finally, an MI-based approach for hydrological dependence modeling was developed. Two types of hydrological processes with different dependence structures were utilized to show the performance of the proposed approach: (i) drought characterization: showing a D-vine structure; and (ii) multi-site streamflow dependence: showing a C-vine structure. Results indicated that the MI-based approach satisfactorily modeled different kinds of dependence structure and yielded more information on variables in comparison with traditional tau-based approach.

Quantifying the change in streamflow complexity in the Yangtze River.

Hydrological processes of the Yangtze River have changed over the past decades due to environmental change and human activity. This paper uses sample entropy to investigate the spatial distribution and dynamic change in streamflow series complexity in the Yangtze River, China. In this study, the complexity of the streamflow series is quantified by entropy analysis. Daily streamflow series for four stations located in the mainstem and two control stations of the two largest freshwater lakes were analysed for the past 60 years. The results showed that the complexity of the streamflow series showed an obvious spatial difference and an increasing trend from upstream to downstream in the Yangtze River. There was a negative relationship between the annual streamflow and the corresponding sample entropy, and their peak-to-valley values showed well-corresponding relationships. The complexity of the runoff series at the Cuntan, Yichang, and Datong stations showed a continuous increasing trend, while that of the Hankou station showed a decreasing trend. The Three Gorges Dam changed the streamflow series complexity in the middle reach of the Yangtze River during the initial impoundment stage, while it had only slight impacts during the fully operational stage. Compared to the mainstem reaches, the streamflow series complexity of the two lakes showed no obvious change. The complexity of the streamflow series in the mainstem of the Yangtze River has been influenced by dam construction. The study could provide a scientific reference for understanding the flow dynamic evolution in the Yangtze River.

Developing a dual entropy-transinformation criterion for hydrometric network optimization based on information theory and copulas.

Hydrometric information collected by monitoring networks is fundamental for effective management of water resources. In recent years, entropy-based multi-objective criterions have been developed for the evaluation and optimization of hydrometric networks, and copula functions have been frequently used in hydrological frequency analysis to model multivariate dependence structures. This study developed a dual entropy-transinformation criterion (DETC) to identify and prioritize significant stations and generate candidate network optimization solutions. The criterion integrated an entropy index computed with mathematical floor function and a transinformation index computed with copula entropy through a tradeoff weight. The best fitted copula models were selected from three Archimedean copula families, i.e., Gumbel, Frank and Clayton. DETC was applied to a streamflow monitoring network in the Fenhe River basin and two rainfall monitoring networks in the Beijing Municipality and the Taihu Lake basin, which covers different network classification, network scale, and climate type. DETC was assessed by the commonly used dual entropy-multiobjective optimization (DEMO) criterion and was compared with a minimum transinformation (MinT) based criterion for network optimization. Results showed that DETC could effectively prioritize stations according to their significance and incorporate decision preference on information content and information redundancy. Comparison of the isohyet maps of two rainstorm events between DETC and MinT showed that DETC had advantage of restoring the spatial distribution of precipitation.

Improved comprehensive ecological risk assessment method and sensitivity analysis of polycyclic aromatic hydrocarbons (PAHs).

Based on the existing comprehensive ecological risk assessment methods of PAHs, this paper proposed an improved hierarchical Archimedean copula integral assessment (HACIA) model with the optimization in the model selection mechanism and accelerating the calculation speed, and according to which performed the sensitivity analysis of the integrated risk relative to the underlying grouped risk probability. Taihu Lake in China and the Bay of Santander in Spain were taken as study areas, whose samples were obtained and extracted concentrations of 16 priority polycyclic aromatic hydrocarbons (PAHs). After briefly analyzing their concentration characteristics and source, their comprehensive ecological risks were evaluated by the improve HACIA model and their sensitivity was also analyzed. The results proved that, for Taihu Lake, pyrogenic sources occupied the dominance, especially grass, coal and wood combustion, while the risk proportion of 5-rings PAHs was the lowest indeed based on the improved HAICA model. For the Bay of Santander, source apportionment indicated both petrogenic and pyrogenic sources, mainly from vehicle emissions including gasoline and diesel engines, and 4-ring PAHs were urgently needed to be managed. However, the sensitivity analysis results of two study areas showed that the most effective control target for reducing integral risk has no obvious relationship with the maximum grouped risk. And a clear linear relationship between the maximum sensitivity range and the logarithm of the initial overall risk only presented in one of study areas, which required further research to clarify. In brief, the improved HACIA model is helpful to evaluate the comprehensive ecological risk of 16 PAHs, and formulate risk management strategies based on grouped risk assessment and sensitivity analysis, with the former points out the admonitory risk and the latter helps to find the most effective mitigation measures.

Evaluation of information transfer and data transfer models of rain-gauge network design based on information entropy.

Rainfall is one of the most fundamental components of the water cycle and is one of the fundamental inputs of hydrological models. A well-designed network can not only depict the regional precipitation characteristics, but also economically yield maximum needed rainfall information. In regions where either there is limited data or data is not available, it is a common challenge to add stations. The entropy theory-based information transfer model and geostatistical interpolation techniques are two solutions to meet the challenge. In this study, we used a representative rain gauge network to do the network design. Two models, based on information transfer and data transfer, were compared for network design. Other rain gauges in the study area were used as reference ("true values") for assessing the model. Results showed that the information transfer model estimated transinformation between station pairs better than did the data transfer model. Different representative gauges were evaluated separately by the directional information transfer index (DIT). The candidate gauges selected with least information redundancy were similar for both information transfer and data transfer models. Though both models captured some least information-redundant areas, other areas may be bypassed because of model errors or estimation errors.

Amorphous Iron(III)-Borate Nanolattices as Multifunctional Electrodes for Self-Driven Overall Water Splitting and Rechargeable Zinc-Air Battery.

Highly stable and low-cost electrocatalysts with multi-electrocatalytic activities are in high demand for developing advanced energy conversion devices. Herein, a unique trifunctional amorphous iron-borate electrode is developed, which is capable of boosting hydrogen evolution, oxygen evolution, and oxygen reduction reactions simultaneously. The amorphous iron borate can self-assemble into well-defined nanolattices on electrode surface through a facile hydrothermal process, which possess more active sites and charge transfer pathways. As a result, the asymmetry overall water-splitting cell that adopts the amorphous electrodes as anode and cathode can be driven at 1.56 V with the current density of 10 mA cm-2 , which is lowest in state-of-the-art catalysts. Moreover, the water-splitting devices can be powered by a two-series-connected amorphous electrode-based zinc-air battery with high stability and Faradic efficiency (96.3%). The result can offer a potential and promising alternative way to develop metal-borate electrode for multifunctional applications.

Mesoporous TiO2 nanosheets anchored on graphene for ultra long life Na-ion batteries.

Sodium-ion batteries, which have a similar electrochemical reaction mechanism to lithium-ion batteries, have been considered as one of the most potential lithium-ion battery alternatives due to the rich reserves of sodium. However, it is very hard to find appropriate electrode materials imputing the large radius of sodium-ion. TiO2 is particularly interesting as anodes for sodium-ion batteries due to their reasonable operation voltage, cost, and nontoxicity. To obtain a better electrochemical property, mesoporous TiO2 nanosheets (NSs)/reduced graphene oxide (rGO) composites have been synthesized via a scalable hydrothermal-solvothermal method with a subsequent calcination process. Benefitting from unique structure design, TiO2 NSs@rGO exhibits a superior cycle stability (90 mAh g-1 after 10 000 cycles at a high current rate of 20 C) and satisfactory rate performance (97.3 mAh g-1 at 25 C). To our knowledge, such ultra long cycle life has not previously been reported.

A new method for wind speed forecasting based on copula theory.

How to determine representative wind speed is crucial in wind resource assessment. Accurate wind resource assessments are important to wind farms development. Linear regressions are usually used to obtain the representative wind speed. However, terrain flexibility of wind farm and long distance between wind speed sites often lead to low correlation. In this study, copula method is used to determine the representative year's wind speed in wind farm by interpreting the interaction of the local wind farm and the meteorological station. The result shows that the method proposed here can not only determine the relationship between the local anemometric tower and nearby meteorological station through Kendall's tau, but also determine the joint distribution without assuming the variables to be independent. Moreover, the representative wind data can be obtained by the conditional distribution much more reasonably. We hope this study could provide scientific reference for accurate wind resource assessments.

A kriging and entropy-based approach to raingauge network design.

Hydrological data, such as precipitation, is fundamental for planning, designing, developing, and managing water resource projects as well as for hydrologic research. An optimal raingauge network leads to more accurate estimates of mean or point precipitation at any site over the watershed. Some studies in the past have suggested increasing gauge network density for reducing the estimation error. However, more stations mean more cost of installation and monitoring. This study proposes an approach on the basis of kriging and entropy theory to determine an optimal network design in the city of Shanghai, China. Unlike the past studies using kriging interpolation and entropy theory for network design, the approach developed in the current study not only used the kriging method as an interpolator to determine rainfall data at ungauged locations but also incorporated the minimum kriging standard error (KSE) and maximum net information (NI) content. The approach would thus lead to an optimal network and would enable the reduction of kriging standard error of precipitation estimates throughout the watershed and achieve an optimum rainfall information. This study also proposed an NI-KSE-based criterion which is dependent on a single-objective optimization. To evaluate the final optimal gauge network, areal average rainfall was estimated and its accuracy was compared with that obtained with the existing rain gauge network.

A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series.

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

A cloud model-based approach for water quality assessment.

Water quality assessment entails essentially a multi-criteria decision-making process accounting for qualitative and quantitative uncertainties and their transformation. Considering uncertainties of randomness and fuzziness in water quality evaluation, a cloud model-based assessment approach is proposed. The cognitive cloud model, derived from information science, can realize the transformation between qualitative concept and quantitative data, based on probability and statistics and fuzzy set theory. When applying the cloud model to practical assessment, three technical issues are considered before the development of a complete cloud model-based approach: (1) bilateral boundary formula with nonlinear boundary regression for parameter estimation, (2) hybrid entropy-analytic hierarchy process technique for calculation of weights, and (3) mean of repeated simulations for determining the degree of final certainty. The cloud model-based approach is tested by evaluating the eutrophication status of 12 typical lakes and reservoirs in China and comparing with other four methods, which are Scoring Index method, Variable Fuzzy Sets method, Hybrid Fuzzy and Optimal model, and Neural Networks method. The proposed approach yields information concerning membership for each water quality status which leads to the final status. The approach is found to be representative of other alternative methods and accurate.

Mid- and long-term runoff predictions by an improved phase-space reconstruction model.

In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall-runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the model are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ''wet years and dry years predictability barrier,'' to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff.

A multidimension cloud model-based approach for water quality assessment.

Lakes are vitally important, because they perform a multitude of functions, such as water supply, recreation, fishing, and habitat. However, eutrophication limits the ability of lakes to perform these functions. In order to reduce eutrophication, the first step is its evaluation. The process of evaluation entails randomness and fuzziness which must therefore be incorporated. This study proposes an eutrophication evaluation method, named Multidimension Normal Cloud Model (MNCM). The model regards each evaluation factor as a one-dimension attribute of MNCM, chooses reasonable parameters and determines the weights of evaluation factors by entropy. Thus, all factors of MNCM belonging to each eutrophication level are generated and the final eutrophication level is determined by the certainty degree. MNCM is then used to evaluate eutrophication of 12 typical lakes and reservoirs in China and its results are compared with those of the reference method, one-dimension normal cloud model, related weighted nutrition state index method, scoring method, and fuzzy comprehensive evaluation method. Results of MNCM are found to be consistent with the actual water status; hence, MNCM can be an effective evaluation tool. With respect to the former one-dimension normal cloud model, parameters of MNCM are improved without increasing its complexity. MNCM can directly determine the eutrophication level according to the degree of certainty and can determine the final degree of eutrophication; thus, it is more consistent with the complexity of water eutrophication evaluation.

Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs.

Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Alleviating the negative environmental impacts of dam-reservoir and balancing the multiple purposes of reservoir operation have attracted wide attention. While previous studies have incorporated ecological flow requirements in reservoir operation strategies, a comprehensive analysis of trade-offs among hydropower benefits, ecological flow, and ecological water temperature demands is lacking. Hence, this study develops a multi-objective ecological scheduling model, considering total power generation, ecological flow guarantee index, and ecological water temperature guarantee index simultaneously. The model is based on an integrated multi-objective simulation-optimization (MOSO) framework which is applied to Three Gorges Reservoir. To that end, first, a hybrid long short-term memory and one-dimensional convolutional neural network (LSTM_1DCNN) model is utilized to simulate the dam discharge temperature. Then, an improved epsilon multi-objective ant colony optimization for continuous domain algorithm (ε-MOACOR) is proposed to investigate the trade-offs among the competing objectives. Results show that LSTM _1DCNN outperforms other competing models in predicting dam discharge temperature. The conflicts among economic and ecological objectives are often prominent. The proposed ε-MOACOR has potential in resolving such conflicts and has high efficiency in solving multi-objective benchmark tests as well as reservoir optimization problem. More realistic and pragmatic Pareto-optimal solutions for typical dry, normal and wet years can be generated by the MOSO framework. The ecological water temperature guarantee index objective, which should be considered in reservoir operation, can be improved as inflow discharge increases or the temporal distribution of dam discharge volume becomes more uneven.