The thermodynamic performance of a regenerative organic Rankine cycle that utilizes low temperature heat sources to facilitate the selection of proper organic working fluids is simulated. Thermodynamic models are used to investigate thermodynamic parameters such as output power, and energy efficiency of the ORC (Organic Rankine Cycle). In addition, the cost rate of electricity is examined with exergo-economic analysis. Nine working fluids are considered as part of the investigation to assess which yields the highest output power and exergy efficiency, within system constraints. Exergy efficiency and cost rate of electricity are used as objective functions for system optimization, and each fluid is assessed in terms of the optimal operating condition. The degree of superheat and the pressure ratio are independent variables in the optimization. R134a and iso-butane are found to exhibit the highest energy and exergy efficiencies, while they have output powers in between the systems using other working fluids. For a source temperature was equal to 120 °C, the exergy efficiencies for the systems using R134a and iso-butane are observed to be 19.6% and 20.3%, respectively. The largest exergy destructions occur in the boiler and the expander. The electricity cost rates for the system vary from 0.08 USD/kWh to 0.12 USD/kWh, depending on the fuel input cost, for the system using R134a as a working fluid.

This study reports the results of exergy, economic and environmental analyses of simple and combined heat and power internal combustion engines. Values of entropy production, second law efficiency are calculated, and an objective function, including initial, operation, maintenance and fuel costs, as well as the external costs of environmental pollutants, such as CO₂, CO and NO_x, are presented for the flue gas of the internal combustion engine. The results show that entropy generation in the combined heat and power mode is 30% lower than that in the simple internal combustion engine. Also, by excessively increasing the air ratio, the system entropy generation decreases in both cases of simple and combined heat and power IC engines. The greatest portion of entropy generation is related to the combined heat and power internal combustion engine. The gas heat exchanger generates more entropy than the jacket heat exchanger. Lower values of electricity cost and external costs of air pollution are provided by higher values of molar air to fuel ratio. The environmental aspects depend on location of the system and time of engine operation.

Management of air pollution in Tehran, Iran, has been a significant challenge for urban authorities in recent years owing to the number and complexity of the factors affecting the formation and spread of the pollutions. The present study used an integrated modeling approach involving Spatio, Temporal, Uncertainty Decision Support Systems (STUDSS) using Multi Criteria Decision Analysis (MCDA) and an Artificial Neural Network (ANN) for the virtual simulation and strategy assessment of air pollution. Since sources of air pollution and associated pollution control strategies are dependent on location, time and uncertain variables, Multi-Dimensional Decision Support System (MDDSS) can be efficient tool for urban air quality decision-making process. In order to model and evaluate air pollution, time-series data over a period of four years, screened and classified management strategies as well as other structural and environmental data such as land uses, terrain topography, heights of buildings, climatic conditions, population density and pollution sources were modeled using advanced software packages of MCDA and ANN. They were ultimately simulated and evaluated using the MDDSS. The results obtained from the implementation of the STUDSS showed that this tool could be used to provide sustainable solutions to air quality in metropolises and could respond to social satisfaction and economic development.