Optimal Operation Research Articles

A Reactor-Network Framework to Model Performance and Emissions of a Longitudinally-Staged Combustion System for Carbon-Free Fuels

Abstract Hydrogen and ammonia are considered crucial carbon-free energy carriers optimally suited for seasonal chemical storage and energy system balance. In this context, longitudinally-staged combustion systems (LSC) represent an attractive technology for achieving low emissions while conserving high load and fuel flexibility at high efficiency. While these two-stage combustion systems have been successfully implemented for natural gas and hydrogen firing of gas turbines, optimal operation with ammonia-based fuel mixtures not yet established. Recent works have shown that ammonia can be combusted with low emissions in gas turbines by employing a Rich-Quench-Lean operational concept where a slightly rich fuel-air mixture is burnt in the first-stage followed by dilution air addition, which completely oxidizes the remaining unburnt fuel. However, an optimal operational strategy to efficiently combust all three fuels within the same system is yet to be established. In this work, we exploit a reactors-network framework to efficiently investigate the emissions performance of a LSC system fired with natural gas, hydrogen and ammonia. Firstly, the framework is validated with experimental and computational emission results in the literature. Secondly, the optimal operational strategy (in terms of fuel- and air-split between the stages) for clean and efficient ammonia-firing operation is found. Thirdly, the consequences of such ammonia-optimized operational strategy on flame stabilization and emissions with natural gas- and hydrogen are considered. Finally, the air and fuel distribution for optimal performance and minimal emissions for all three fuels is found for the LSC system.

Real time implementation of scaled droop control in hybrid microgrid with hydrogen storage for regulation of voltage and frequency.

The incorporation of renewable energy resources (RERs) into smart city through hybrid microgrid (HMG) offers a sustainable solution for clean energy. The HMG architecture also involves linking the AC-microgrid and DC-microgrid through bidirectional interconnection converters (ICC). This HMG combines AC sources like wind-DFIG with DC sources such as solar PV and solid oxide fuel cell (SOFC), supported by battery energy storage systems (BESS) and hydrogen storage units (HSU). The HSU can generate and store hydrogen during RER surplus. This stored hydrogen can be further employed for production of electrical power along with numerous other applications. The HSU is emerged as a competent tool which can be utilised alone/in combination with BESS to enhance the system reliability. Harvesting power from clean and green sources requires its optimal operation and control while feeding to the existing grid. The existing strategies of controlling ICC are complex and not efficient; hence, a novel intelligent scaled droop control structure (SDCS) is proposed, utilizing frequency, DC voltage, and active power. The SDCS regulate voltage and frequency in both islanded mode (IM) and grid connected mode (GCM) of HMG. Experimental validation demonstrates its simplicity and effectiveness, making it suitable for smart city environments, ensuring uninterrupted power for critical loads with improved air quality.

Evaluation of Maturity in Environmental, Social, and Governance (ESG) Risks Mitigation and Safety Climate in the Oil and Gas Company, Bojonegoro

Environmental, Social, and Governance (ESG) has become a rapidly growing instrument worldwide, driven by commitments to enhance environmentally sustainable economic growth as outlined in the 2015 Paris Agreement. Companies in Indonesia are increasingly emphasizing the importance of ESG in their business activities due to the potential impact on the environment and society, including the oil and gas companies. Environmental and social risks can affect the sustainability of a company, while safety climate plays a role in the safety and well-being of workers. The study used a descriptive qualitative approach. It also used NOSACQ-50 questionnaires to assess the safety climate and identify environmental and social management system (ESMS) elements using the toolkit from the International Finance Corporation (IFC) at the Bojonegoro oil and gas company. The result of the ESMS IFC matrix concludes that the maturity of Bojonegoro’s oil and gas company in mitigating ESG risks needs to be more prepared to mitigate ESG risks as preparation progresses towards optimal operations, whilst the safety climate has an average score of 3.40, which means that the safety climate can be maintained and developed.

Spectrofluorimetric and smartphone-based detection methods for determination of gentamicin

AbstractThis paper presents the development of spectrofluorimetric and smartphone-based detection methods for gentamicin determination using fluorescamine as a reagent. The research included selecting excitation (415 nm) and emission (489 nm) wavelengths, reaction time, and conditions like reagent concentration, and pH of the reaction medium. Moreover, the optimal operating parameters of the smartphone camera, like ISO, white balance, camera shutter, and RGB model channel were selected. Analytical parameters of the developed spectrofluorimetric and smartphone-based methods were estimated including the linear range: 0.04–15.00 mg dm−3 and 0.18–1.20 mg dm−3, respectively, limits of detection and quantification: 0.01 and 0.04 mg dm−3, and 0.06 and 0.18 mg dm−3, respectively, and precision (CV, n = 6): 5.2% and 2.8%, respectively. The proposed approaches were successfully applied to determine gentamicin in pharmaceutical samples. The obtained results were consistent with values declared by manufacturers and satisfactory recovery values, 93.2–113.6% were obtained for both spectrofluorimetric and smartphone-based methods. The developed fluorimetric method with smartphone-based detection provides a low limit of detection specific to spectrofluorimetric methods whereas the measurement system is a simple, easily accessible, compact, and low-cost device. Hence, it can become a competitive alternative to other gentamicin determination methods. Graphical abstract

Financial Hedging of Operational Risk Constraints: A General Framework

We consider the operation of a non-financial corporation and present a novel risk-management framework that supports the joint optimization of the firm’s operational and financial decisions. Specifically, we study the problem of maximizing the firm’s operating profits when these profits depend on movements in the financial markets, and where the decision maker—the firm’s manager—is risk averse. Critically, our framework (i) imposes risk aversion through a generic class of risk constraints, and (ii) considers explicitly the differences that can exist between the shareholders’ and manager’s beliefs about future market risks. Together, these aspects allow us to remove speculation so that financial instruments can only be used for hedging purposes. We analyze our problem in an incomplete financial market setting and consider different informational assumptions. Within this framework, we characterize the optimal financial hedging strategy so that the problem reduces to a constrained maximization over operational policies. Our results show how financial hedging can be effectively used to expand the set of admissible operating policies, and identify conditions under which the risk constraint can be completely ignored when solving for an optimal operating policy. We illustrate our methodology in the context of an exporter firm which faces exchange rate risk in trading.

Resonant switching current detector based on underdamped Josephson junctions

Current-biased Josephson junctions can act as detectors of electromagnetic radiation. At optimal conditions, their sensitivity is limited by fluctuations causing stochastic switching from the superconducting to the resistive state. This work provides a quantitative description of a stochastic switching current detector, based on an underdamped Josephson junction. It is shown that activation of a Josephson plasma resonance can greatly enhance the detector responsivity in proportion to the quality factor of the junction. The ways of tuning the detector for achieving optimal operation are discussed. For realistic parameters of Nb/AlOx/Nb tunnel junctions, the sensitivity and noise-equivalent power (NEP) can reach values of S≃5×1012 (V/W) and NEP≃2×10−23 (WHz−1/2), respectively. These outstanding characteristics facilitate both bolometric and single-photon detection in microwave and terahertz ranges. Published by the American Physical Society 2024

Leveraging the dynamics of microalgal CO2 capture to estimate the maximum inherent photosynthetic potential

AbstractBACKGROUNDNatural photosynthesis, utilizing intelligent molecular machinery to harness sunlight, stands as the benchmark for efficient energy generation. Despite its high quantum efficiency compared to synthetic methods, challenges persist due to dynamic nutritional and light requirements in plant, microalgae, and cyanobacteria‐driven processes. Reported microalgae CO2 uptake rates rely on biomass dry cell weight measurement after certain incubation period and systematic study of required CO2 concentrations for optimal photobioreactor operation remaining unexplored. This research is thus aimed at evaluating the critical dissolved CO2 (Ccrit ∙ CO2) concentration and specific CO2 uptake rates (SCUR) of Chlorella minutissima (CM) culture in the presence of surplus light and other nutrients by online monitoring and measuring the dynamic CO2 uptake rates which will help in maintaining the optimal rate of CO2 delivery for continuous cultivation of microalgae while minimizing the escape of CO2.RESULTSDissolved Ccrit ∙ CO2 was determined to be in the range of 16–20 mg/L, and the maximum SCUR in BBM (Bold Basal Medium) was found to be 0.73 mg CO2 (mg dcw ∙ h)−1. Interestingly, this SCUR value is nearly three times greater than that of the most efficient in vitro artificial photosynthetic novel pathway reported so far.CONCLUSIONFrom the calculated SCUR values, it may be noted that 9.6 g of biomass can be obtained from 1 g of microalgal inoculum in a day, thereby setting a benchmark for the scientists working in the areas of bioprocess engineering, synthetic biology, and metabolic engineering to comply. © 2024 Society of Chemical Industry (SCI).

Hydroprocessing Microbial Oils for Advanced Road Transportation, Aviation, and Maritime Drop-In Fuels: Industrially Relevant Scale Validation

Triacylglycerides (TAGs) produced via the syngas fermentation of biogenic residues and wastes were evaluated as a potential feedstock for advanced road transportation, aviation, and maritime drop-in fuels via hydroprocessing technology. Due to the limited availability of TAGs, a simulated feedstock (SM TAGs) was utilized by blending various commercial oils, simulating the fatty acid composition of TAGs. At first, the simulated feedstock and the real TAGs were hydrotreated on a TRL 4 (technology readiness level) pilot plant to evaluate the potential of the SM feedstock to simulate the TAGs based on product quality. The hydrotreatment technology was evaluated and optimized on a TRL 4 plant. The research was further extended to a TRL 5 hydrotreatment plant with the optimum operating window for scaling up the technology. The resulting product was fractionated on a batch fractionation unit under vacuum to separate the jet and diesel fractions. The produced fuels were analyzed and evaluated based on the aviation Jet A1, EN590, EN15940, and marine diesel DMA specifications. The results show that the TAG composition was successfully simulated via a blend of vegetable oils. In addition, the hydrotreatment of the real TAGs and simulated feedstock resulted in similar-quality liquid products. The technology was successfully scaled up on a TRL 5 unit, leading to advanced, high-quality aviation and diesel drop-in fuels from TAGs, while the reaction pathways of hydrotreating can be controlled via the operating parameters of pressure, temperature, and H2/oil ratio. The hydrotreatment process’s optimum conditions were 13.8 MPa pressure, 643 K temperature, 1 h−1 liquid hourly space velocity (LHSV), and 5000 scfb hydrogen-to-oil ratio. Finally, a storage stability study of the hydrotreated liquid product showed that it can be stored for more than 6 months at ambient conditions without any noticeable changes to its properties.

Разработка технологии получения безлактозного молока методом диафильтрации

Abstract. The purpose. The research is aimed at solving the problem related to the development of technology for producing an environmentally friendly product of animal origin – lactose-free milk. This will solve the problem of consumption of milk and dairy products by people who do not tolerate milk sugar for physiological reasons. Analyzing, on the basis of a literary review, the current state of the issue of lactose-free milk production, it can be concluded that existing technologies have certain disadvantages due to changes in the composition of the initial product, or the significant complexity of the technological chain. The proposed development is based on a method of milk diafiltration consisting of several repetitive cycles. The cycle consists in the separation of milk by the ultrafiltration process into permeate (aqueous lactose solution) and concentrate (proteins, fat, lactose residues). Then pure water is added to the concentrate in a volume equal to the volume of the discharged permeate. For a scientifically based approach to the development of the proposed technology, it is necessary to conduct a study of ultrafiltration separation of milk, obtain optimal operating parameters of the process, determine the change in permeability and selectivity of membranes from lactose concentration, consider the possibility of obtaining lactose-free milk with different mass fraction of fat, determine the number of cycles of the membrane diafiltration process. Research methods. The study of the process of ultrafiltration separation of milk was carried out in laboratory conditions on a membrane installation using organic and inorganic membranes. The experiments determined the main characteristics of ultrafiltration membranes – permeability and selectivity, with varying parameters (parameter range: P = 0.15…0.5 MPa, t = 35…65 °C). Results. Based on the experimental data obtained, by processing them and optimizing the parameters, optimal modes of the milk separation process by ultrafiltration were obtained. The following parameters are defined: the velocity of the product in the supramembrane space, the operating pressure of the separation process, its temperature. The preferred ultrafiltration membranes for the membrane-milk system have been selected. Scientific novelty. To develop a technology for producing lactose-free milk, for the first time a number of studies and experiments were carried out, such as changing the permeability and selectivity of membranes from the concentration of lactose, obtaining lactose-free milk with mass fraction of fat the number of cycles of the membrane diafiltration process was determined.

Dynamic Operations of a Mobile Charging Crowdsourcing Platform

This paper investigates the operation of a novel electric vehicles (EVs) charging service mode, that is, crowdsourced mobile charging service for EVs, whereby a crowdsourcing platform is established to arrange suppliers (crowdsourced chargers) to deliver charging service to customers’ electric vehicles (parked EVs) at low-battery levels. From the platform operator’s perspective, we aim to determine the optimal operation strategies for mobile charging crowdsourcing platforms to achieve specific objectives. A mathematical modeling framework is developed to capture the interactions among supply, demand, and service operations in the crowdsourced mobile charging market. To design an efficient solution method to solve the formulated model, we first analyze the model properties by rigorously proving that a crucial variable set for operating the mobile charging crowdsourcing system includes charging price, commission control, and period-specific aggregate demand control. Besides, we provide both an equivalent condition and a necessary condition for checking the feasibility of these crucial variables. On top of this, we construct a search tree according to the operation periods in a day to solve the optimal operation strategies, wherein a nondominated principle is adopted as an accelerating technique in the searching process. The solution obtained from the proposed solution algorithm is proved to be sufficiently close to the actual global optimal solutions of the formulated model up to the resolution of the discretization scheme adopted. Numerical examples provide evidence verifying the model’s validity and the solution method’s efficiency. Overall, the research outcome of this work can offer service operators structured and valuable guidelines for operating mobile charging crowdsourcing platforms. Funding: This work was supported by the Singapore Ministry of Education [Grant RG124/21]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0126 .

Response Surface Methodology Approach for Modeling and Performance Optimization of a PEM Fuel Cell

A Polymer Electrolyte Membrane (PEM) fuel cell is a popular green source of electrical energy and is often used in applications like electric vehicles due to its environmentally friendly operation. This type of fuel cell has a low operating temperature, light weight, and negligible emission of greenhouse gases. However, the PEM fuel cell is a complex multivariable system with a large number of input and output factors, and most of the input factors affect output factors directly or indirectly. As a result, it is conventionally quite difficult to determine which input factor has a major effect on a particular output factor. Statistical methods are very popular for finding the individual and interaction effects of input factors on output factors. In this paper, for the first time, a simple and realistic MATLAB SIMULINK model for a PEM fuel cell is presented to conduct various experimental tests. The developed MATLAB SIMULINK model and statistical design of experiments, Response Surface Methodology (RSM), are used to develop metamodels (mathematical models of the simulation model) for the PEM fuel cell to find the individual and interaction effects of various input factors on output factors. The developed metamodels can be used to find the region for optimum operation of the presented fuel cell. The metamodels are validated by conducting four different statistical tests. The optimum point of operation is presented by calculating stationary points from the metamodels.

A Low-Carbon Collaborative Optimization Operation Method for a Two-Layer Dynamic Community Integrated Energy System

The traditional centralized optimization method encounters challenges in representing the interaction among multi-agents and cannot consider the interests of each agent. In traditional low-carbon scheduling, the fixed carbon quota trading price can easily cause arbitrage behavior of the trading subject, and the carbon reduction effect is poor. This paper proposes a two-layer dynamic community integrated energy system (CIES) low-carbon collaborative optimization operation method. Firstly, a multi-agent stage feedback carbon trading model is proposed, which calculates carbon trading costs in stages and introduces feedback factors to reduce carbon emissions indirectly. Secondly, a two-layer CIES low-carbon optimal scheduling model is constructed. The upper energy seller (ES) sets energy prices. The lower layer is the combined cooling, heating, and power (CCHP) system and load aggregator (LA), which is responsible for energy output and consumption. The energy supply and consumption are determined according to the ES energy price strategy, which reversely affects the energy quotation. Then, the non-dominated sorting genetic algorithm embedded with quadratic programming is utilized to solve the established scheduling model, which reduces the difficulty and improves the solving efficiency. Finally, the simulation results under the actual CIES example show that compared with the traditional centralized scheduling method, the total carbon emission of the proposed method is reduced by 16.34%, which can improve the income of each subject and make the energy supply lower carbon economy.

Study on separation of 6–0 mm low rank high sulfur lignite by FGX dry separation bed

ABSTRACT Coal is an essential primary energy for the national economy and people’s livelihood. In recent years, with the rapid growth of the consumption of high-quality coal resources, Lignite with a low degree of metamorphism has been extensively exploited and utilized. In this paper, desulfurization and ash reduction treatment of 6–0 mm fine-grade low-grade high-sulfur Lignite was carried out using a FGX dry separation bed (FGX FGX compound dry separation bed). The migration and distribution of differentiated density particles in the bed under the action of cascade air flow were mainly studied, and the influence of vibration intensity (amplitude and frequency) on the separation effect of fine-grade low-grade high-sulfur Lignite was explored. At the same time, the material distribution characteristics of each section in the direction of the discharge edge of the bed were systematically analyzed, the material distribution area of the bed was determined, the optimal operating parameters (A = 2.8 mm, f = 26 Hz, V = 2.29 m/s) were obtained, and the sorting test was carried out under the optimal parameter conditions. The test results show that when the vibration frequency f = 26 Hz, the sulfur segregation degree of bedding materials reaches the maximum Ss = 0.36, and the sulfur content of materials in I, II, and III regions is 0.85%, 5.47%, and 18.78%, respectively. The stratification effect of bedding materials is the best. When A = 2.8 mm, the material sulfur segregation reaches the minimum value of Ss = 0.28, and the average sulfur content of materials in I, II, and III regions is 0.78%, 5.46% and 18.27%, the material is stratified according to density. When the airflow rate is 2.55 m/s. The stratification effect of bed material is best according to density. The clean coal yield is 69.87%, and the sulfur content is 0.98%. The gangue yield is 30.13%, and the sulfur content is 18.66%. The Ep value of energy deviation is 0.11 g/cm3, which realizes high precision dry desulfurization and upgrading fine-grade, low-grade, and high sulfur lignite.

Thermoelectric properties enhancement and optimization of SnTe-based material with single doping: RSM-ANN approach

Tin Telluride (SnTe) stands out among semiconductor materials, drawing attention as an environmentally friendly alternative to conventional Lead Telluride (PbTe). This study explores the novel incorporation of titanium (Ti) and zirconium (Zr) as dopant elements in SnTe-based materials, an approach that has never been previously investigated. Leveraging mathematical modeling and machine learning methodologies, we use Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) to predict thermoelectric properties of SnTe-based materials and find the optimum dopant element addition and operating temperature. Eleven samples featuring varying Ti and Zr concentrations are employed in experimental investigations, with dopant amounts ranging from 0 to 0.05 M. Characterization of each sample is conducted across a temperature spectrum from 50 to 450 °C, yielding a comprehensive dataset of 99 entries for neural network model construction. The generated RSM and ANN models demonstrate remarkable predictive accuracy for the thermoelectric properties of SnTe-based materials, showcasing coefficient of determination (R2) values spanning from 0.986 to 0.998. This study uses RSM and ANN-GA to predict an optimized zT value of 0.558. This value surpasses the highest zT value from the experiment by 3.33 %. In conclusion, this investigation underscores the potency of RSM and ANN as valuable tools for advancing research on SnTe-based thermoelectric materials, exemplifying their capacity to hasten the exhausting process of finding the optimum dopant addition and operating temperature.

Optimizing wind-solar hydrogen production through collaborative strategy with ALK/PEM multi-electrolyzer arrays

This paper presents an innovative sustainable hydrogen production system integrating solar and wind energy to provide power to alkaline (ALK) and proton exchange membrane (PEM) electrolyzers. On this basis, a novel source and load collaborative optimization strategy is proposed to optimize the hydrogen production system, aiming to address the challenges of the intermittent and variable nature of renewable energy sources. The proposed strategy consists of a classification prediction module using the XGBoost algorithm, which analyzes historical energy generation data to predict optimal electrolyzer operation. Furthermore, the proposed collaborative optimization strategy is able to balance the loads between the electrolyzer arrays, aiming to minimize cold-start situations, prolong steady-state hydrogen production time, and reduce operational variations due to maintenance. The proposed strategy is proved to significantly reduce the standard deviation of the electrolyzer runtime, enhancing the overall system stability, and contributing to the reduction of maintenance frequency. Theoretical analyzes and extensive simulation results validate the effectiveness of the proposed strategy in improving hydrogen production efficiency and sustainability. This work contributes to the integration of renewable energy sources for clean energy production, and provides a practical and effective solution for the urgently needed green hydrogen production.

Reduced order modeling for optimal aerodynamic design and operation of the industrial air-jet ejector

Reduced order modeling for optimal aerodynamic design and operation of the industrial air-jet ejector

Sustainable machining of AISI4140 steel: a Taguchi-ANN perspective on eco-friendly metal cutting parameters

This work explores environmentally conscious machining practices for AISI4140 steel through Taguchi analysis. The study employs a design of experiments (DOE) approach, focusing on cutting speed, depth of cut, and coolant type as parameters. Taguchi’s L9 orthogonal array facilitates systematic experimentation, and the results are analyzed using MINITAB 17 software. Signal-to-noise ratios (SNR) are utilized to establish optimum operating conditions, evaluate individual parameter influences, and create linear regression models. The experiments reveal neem oil with graphene coolant as an eco-friendly solution, addressing health and environmental concerns. Main effects plots visually represent the impact of parameters on machining quality. Additionally, regression and artificial neural network (ANN) models are compared for surface roughness prediction, with ANN showing superior performance. The findings advocate for optimized cutting conditions, emphasizing material conservation, enhanced productivity, and eco-friendly practices in AISI4140 steel machining. This research contributes valuable insights for industries seeking sustainable machining solutions.

Improved thermal stability and electrical conductivity of dysprosium doped barium zirconium cerate electrolyte

Acceptor-doped perovskites based on the BaCeO3 have been extensively investigated as promising candidates for proton-conducting electrolyte at intermediate temperatures. This operational regime is characterized by proton conduction facilitated via hydroxyl protons that effectively occupy vacancies of oxide ions within the perovskite crystal lattice structure. While the trade-off parameters (proton conductivity and chemical stability) concern the utility of cerium-based electrolytes, acceptor doped compounds hinder the mobility of protons due to dopant-charge and dopant-host interactions. However, Dy-doped compounds reveal a promising behavior relative to most acceptor substituents with marginal constraints at higher doping concentration. As a result, a threshold doping range is essential to combat material inconsistencies such as cationic disorder to proton trapping effect. Therefore in the present study we synthesize the single-phase dysprosium and zirconium co-doped barium cerate at different doping concentrations (BaCe0.8-xZr0.2DyxO3-δ (x = 0.0–0.20)) to showcase the chemical inertness of the compound against oxidizing and reducing atmospheres preserving high ionic conductivity. According to our experimental outcomes, higher ionic conductivity among wet atmospheric conditions suggests the role of moisture to contribute additional charge carriers among the Dy-doped samples via explicitly generated oxygen vacancies. As a result, BaCe0.65Zr0.2Dy0.15O3-δ demonstrates a highest total conductivity, reaching 2.44 x 10-2 S cm−1 at 575 °C, with an associated activation energy of 0.61 eV within the temperature range of 300–600 °C. The study also reveals the optimal operating temperature of the proton conducting electrolyte to avoid mixed ionic (H+ and O2–) conduction.

Rapid identification of back-flow in three distributaries of Jingjiang Reach, middle Yangtze River, China

Unusual back-flow events occurred in 2015 and 2017 at the near-mainstream ends of the three distributaries of the Jingjiang Reach, middle Yangtze River, China. It is presently challenging to precisely quantify back-flow discharges at river distributary ends where normal-flow discharges are measured chronically. This problem is more complicated at the ends of the three Jingjiang distributaries, each of which is more than 100 km long, and thus favors the generation of different back-flow forms. We use empirical formulae based on the Saint-Venant Equation and the Fall Index Method to quantify impacts on flow discharges at the two ends of each distributary of three key hydrodynamic variables: (1) water discharge at Zhicheng hydrological station, (2) total discharge from the four southwest tributary rivers, and (3) water levels at the two ends of the distributaries. We use the variable control approach to identify the contribution ratios of natural and human driving factors to flow discharges at the two ends of the distributaries during five historical stages. The empirical formulae provide reasonable results, with high values of correlation coefficients (R2 = 0.84 ∼ 0.98) obtained, demonstrating that the total water discharge from the four southwest tributary rivers is negligible. These formulae perform well in reproducing observed flow discharges at the two ends of the distributaries, especially the maximum discharges and cumulative volumes for normal-flow and back-flow events. Three charts are devised for different flow conditions at the two ends, from which back-flow events can be rapidly identified. Before impoundment of the Three Gorges Dam (TGD), riverbed adjustment caused by the lower Jingjiang Cutoff Projects and the Gezhou Dam contributed the most to flow discharge changes (≥50 %) at the near-mainstream ends of the distributaries. Afterwards, precipitation dominated (>60 %). In the future, back-flow events are expected only to occur at Mituosi (i.e., Taipingkou) and Nanxian hydrological stations, for which optimal reservoir operation and water resource utilization could remedy the back-flow crisis. Use of climate models and more advanced technologies such as remote sensing and GIS are recommended to enhance the reliability of predictions.

Energy, Exergy, Economic, and Environmental Compromising Performance of Dual-stage Evaporation-Ammonia Hybrid Compression-Absorption Refrigeration System for the Cooling Supply of Keto-Benzene Dewaxing Process

Absorption refrigeration systems driven by low-temperature waste heat is one way to achieve “carbon neutrality”. Meanwhile, the keto-benzene dewaxing equipment needs cooling capacity of 5 MW which refrigeration temperature of –10 °C and –25 °C. This paper researches the feasibility of DSE-AHCARS (Dual-stage Evaporation-Ammonia Hybrid Compression-Absorption Refrigeration System) replacing the vapor compression refrigeration system for keto-benzene dewaxing process based on 4E (Energy, Exergy, Economic, and Environmental) analysis. At the primary and secondary stage evaporation temperature of 0 and –23 °C, COP reaches the maximum value of 0.85. However, COP-Electricity reaches the minimum value of 8.1. When the secondary stage refrigeration temperature is –23 °C, CO2 emission increases from 1150 t·a–1 to 3600 t·a–1, and Life Cycle Climate Performance increases from 3.29×104 tons to 7.7×104 tons with the primary stage refrigeration temperature is –15 °C to 0 °C. As well as matching three parameters to ensure the 4E compromising performance by the multi-objective optimization. To guarantee the Life Cycle Climate Performance is less than 5.5×104 tons, the payback period is less than 2 yr, and COP is greater than 0.6 at the optimal operation ranges that refrigeration temperature difference between primary stage and secondary stage is within 20 °C. The power of DSE-AHCARS was reduced by 77% compared with the vapor compression refrigeration system. Therefore, the DSE-AHCARS can reduce CO2 emissions by about 6250 t·a–1 and save 1.2×105 tons of CO2 in the Life Cycle Climate Performance term. This result shows that the DSE-AHCARS can completely replace the vapor compression refrigeration system.