Search

This study focuses on the Wartsila 9L34DF engine and proposes an integrated system for low-temperature carbon capture using the coupling of cold and hot energy recovery with membrane separation in LNG-powered ships. By utilizing a series dual-pressure organic Rankine cycle (SDPORC) system to recover waste heat from the engine exhaust gases and generate electricity, the system provides power support for the low-temperature carbon capture compression process without consuming additional ship power. To validate the accuracy and reliability of the mathematical model, the simulation results are compared with the literature's data. Once the model's accuracy is ensured, the operational parameters of the integrated system are analyzed. Subsequently, working fluid optimization and genetic algorithm sensitive parameter optimization are conducted. Finally, under the optimal operating conditions, the thermodynamic performance and economic evaluation of the integrated system are assessed. The results demonstrate that the net power output of the integrated system is 100.95 kW, with an exergy efficiency of 45.19%. The unit carbon capture cost (UCC) is 14.24 $/ton, and for each unit of consumed LNG, 1.97 kg of liquid CO2 with a concentration of 99.5% can be captured. This integrated system significantly improves the energy utilization efficiency of ships and reduces CO2 emissions. [ABSTRACT FROM AUTHOR]

An important engineering challenge is the design of a wind turbine's pitch angle controller. The dependability, safety, and power output maximization of a wind turbine are all impacted by this controller. In this study, a 2 MW doubly fed induction generator wind turbine's blade angle controller design with a novel fuzzy logic controller is tested in a simulated environment. The evolutionary algorithm technique is used to optimize the fuzzy logic controller with three inputs. A genetic algorithm is used to optimize the specified pitch angle controller for a number of coefficients. After the optimization process, the controller's performance is assessed in terms of power output, overshoot, and steady-state error characteristics. [ABSTRACT FROM AUTHOR]

Under the "carbon peaking and carbon neutrality" goal, Shanxi Province adjusts the power supply structure and promotes the development of a high proportion of new energy, which has a certain impact on the demand for thermal coal. Therefore, constructing a reasonable forecasting model for thermal coal demand can play a role in stabilizing coal supply and demand. This paper analyzes various factors related to coal demand, and uses Pearson coefficient to screen out six variables with strong correlation. Then, based on the scenario analysis method, combined with the "14th Five-Year Plan" of Shanxi Province, different scenarios of economic development and carbon emission reduction development are set. Finally, a multi-scenario GA–LSSVM forecasting model of thermal coal demand in Shanxi Province is constructed, and the future development trend of thermal coal demand in Shanxi Province is predicted. The results show that the demand for thermal coal is the largest in the mode of high-speed economic development and low emission reduction, and the demand for thermal coal is the lowest in the mode of low-speed economic development and strong emission reduction, which provides a scientific basis for the implementation of Shanxi Province's thermal coal supply policy. [ABSTRACT FROM AUTHOR]

An integrated electrical and thermal residential renewable energy system consisting of solar thermal collectors, gas boiler, fuel cell combined heat and power, a photovoltaic system with battery, inverter, and thermal storage for a single-family house of Sonnenhaus standard is investigated with a model predictive controller (MPC). The main focus of this article is to define a multi-objective mathematical function, develop a coupled simulation framework for the nonlinear time-varying deterministic discrete-time problem of the energy system using TRNSYS and MATLAB. With the developed methodology, a sensitivity analysis of maximum optimization time, swarm (or population or mesh) size of a typical spring day and a typical summer day assuming a 100% accurate weather and load forecast with three different algorithms: particle swarm optimization (PSO), genetic algorithm (GA) and global pattern search (GPS) are analyzed. Finally, the obtained results are compared with a status quo controller. Results show that the PSO algorithm optimizer performs the best in this MPC for such a complex and time-consuming MPC model in both the spring day and the summer day. The obtained results show that the PSO with swarm size 50 in the selected typical spring day and the PSO with swarm size 40 in the selected summer day reduces the objective function's fitness value from 413 to −177 within 6 h optimization time and from 1396 to 1090 in 4 h optimization time respectively. [ABSTRACT FROM AUTHOR]