Evaporative cooling towers are critical solutions to engineering design. Due to the complexity of the heat rejection mechanism and the commonality of the evaporative cooling application, there exists a significant opportunity for efficiency improvements. To mitigate the complexity issue, a model was developed that focuses on the quantification of benefits for efficiency projects. This tool can quantify the reductions in water usage, energy consumption, carbon dioxide emissions, and operational costs for industrial cooling towers for various projects using dynamic, hourly weather data inputs. A validation study using an industrial cooling tower was completed to compare the model and field data. The validation study completed by field data collection showed that the model's emphasis on simplicity does not negatively impact the accuracy of the model's outputs. A distinctive feature of this model is the ability to estimate the tower performance from design parameters and hourly weather conditions through various efficiency projects to the resulting energy, water, carbon dioxide emissions, and cost reductions. Since a cooling tower is a heat rejection device, the cooling capacity is a critical parameter for operation. This parameter was studied through the measured quantities for air discharge temperature, cooling tower fan power, and makeup water flowrate where the model predicts difference values with the field data of 4.3%, 4.7%, and 7.6%, respectively. Another parameter studied was the cooling efficiency, or approach, which the model predicts an average difference of 14% compared to the field data. This is a parameter that is highly dependent on physical design conditions for the cooling tower. Finally, the resultant parameters of the electrical and water consumptions were studied through the fan power and makeup water flowrate, respectively.
Due to the synergistic impact of high pressure within deep shale reservoirs, intricate interactions among gas moleculars, and the coupled influence between gas moleculars and pore walls, the migration of gases in nanopores experiences significant constraints. In this study, by employing the molecular dynamics simulation techniques, the migration process of multicomponent alkane molecular is conducted in deep shale reservoirs. And then, by adopting the tensile molecular dynamics simulation method, a high-pressure-driven model of shale gas migration in deep shale reservoirs is established, elucidating the migration behavior and dynamic mechanisms of CO2-multicomponent gases within the inorganic nanopores in deep shale reservoirs. Meanwhile, based on the spring force suffered by gas when it migrates in inorganic nanopores, the gas migration process in deep shale reservoirs can be divided into three stages (the adjacent stage of the gas moleculars, the migration stage of gas moleculars, and the relaxation stage of gas molecule migration). The resistance mechanism of the gas during migration is further clarified. The results show that the gas–solid interaction and capillary pressure in deep shale reservoirs predominantly impede the migration of multicomponent gases within inorganic nanopores. For the small-scale pore simulation system, the gas–solid interaction effect is significantly improved in particular.
Large cities in cold climates face profound challenges in reducing carbon emissions while meeting substantial heating demands-particularly where dense, aging building stock relies on conventional fuels. This study situates New York City (NYC) within a comparative global analysis of cities with populations over 2 million with high heating degree days (HDD), population densities, and total heating burdens. NYC, which has committed to reducing greenhouse gas (GHG) emissions 80% below 2005 levels by 2050, serves as a case study for the feasibility of deep decarbonization through building electrification. Based on 2022 verified energy consumption data, US Environmental Protection Agency emissions factors, and modeled future scenarios, our analysis shows that buildings accounted for 62% of NYC's emissions-primarily from natural gas and fossil fuel-based electricity. Full electrification of space and water heating would add 29.96 terawatt-hours (TWh) to annual electricity demand, an 80% increase over 2022 levels. However, expected renewable additions from upcoming and proposed solutions, Champlain Hudson Power Express (10.4 TWh), Empire Wind I (2.43 TWh), and rooftop solar (11.6 TWh), would cover only 37% of this new demand. Achieving an 80% reduction in building emissions would require that 76.8% of electricity be supplied by low-emission sources. In the context of international peers with similar heating and density profiles, NYC's trajectory reveals the scale of infrastructure acceleration needed. These findings offer a quantitative framework for assessing urban building decarbonization across high-demand global cities.
In deep-sea oil and gas exploitation, the reliability of mud lifting pumps directly affects the operational safety. However, the coupled operation of multiple pumps may easily induce composite faults such as abnormal vibration and excessive torque, which the conventional diagnostic methods struggle to address effectively. This article proposes an improved support vector machine (SVM) diagnostic method based on the collaborative optimization of linear discriminant analysis (LDA) and adaptive differential evolution (ADE). It uses LDA to reduce the dimensionality of the dataset and extract fault-sensitive features, and dynamically optimizes the kernel function parameters of SVM through the ADE algorithm, thereby constructing the LDA–ADE–SVM hybrid diagnostic model. To verify its effectiveness, a ten-stage centrifugal pump fluid–structure coupling model was built. Eighty-one groups of orthogonal experiments were designed to obtain 81 groups of simulation data. Compared with SVM, LDA–SVM, and LDA–PSO–SVM, the LDA–ADE–SVM achieved an accuracy of 96.30% with a calculation time of 13.93 s, outperforming the other algorithms. A dual-pump test platform was built to simulate dynamic working conditions. Fifty-four groups of orthogonal experiments were completed to collect 9294 samples, which were divided into groups at a ratio of 7:3 for comparison. The LDA–ADE–SVM showed significant advantages with an accuracy of 98.27% and a calculation time of 1028.16 s. This verifies its adaptability and robustness, providing support for offshore drilling and oil-gas development safety.
This study proposes a novel optimization method to enhance the behavior of organic Rankine cycle (ORC) systems through utilizing zeotropic mixtures with temperature glide characteristics. The mixture is created by introducing a pure fluid with thermodynamic behavior similar to the target medium but capable of producing a larger temperature glide. Using R245fa as the reference fluid, the behavior of the candidates was examined through simulations and further validated on an experimental ORC test bench. Results show that the ORC system achieved its optimal behavior when the heat source, cooling water, and cycle medium mass flows are 4240 kg/h, 3000 kg/h, and 700 kg/h, respectively. Under these conditions, the turbine enthalpy drop reaches 10.29 kJ/kg, the net output power is 1.919 kW, and the thermal and exergy efficiencies are 12.6% and 3.53%, respectively. With the cycle medium flow range of 500–700 kg/h, the 0.3R134a/0.7R245fa mixture delivers consistently higher efficiencies than pure R245fa, confirming the mixture's performance advantage under practical low-temperature waste-heat conditions.
