Generally, buildings are the greatest energy consumer in the world. To satisfy their energy needs, a comprehensive solution should be considered for power generation, gas distribution network and power transmission lines. Nowadays by advent of new technologies, distributed generation seems to be a proper way. Moreover, residential sector not only is the main power consumer, but also has the highest share of gas consumption, so each effort for reclaim energy demand in buildings has tremendous effect on power and energy market. Distributed Generation (DG) technologies include electric power generation by Engines, Small turbines, Fuel cells, Photovoltaic systems and other small Renewable generation technologies. In this research, distributed generation based on gas engines and micro turbine introduced as a reliable solution generally for countries that have an access to Natural Gas or Bio Gas and particularly for Iran which has extended Natural Gas distribution network. In this paper, first the conditions of Iran energy sources, power generation and energy consumption sectors are studied in brief. Next, Energy Consumption Criteria (ECC) and Daily Consumption Chart (DCC) introduced as the graphical powerful tools for DG systems design and modeling. Each DG recommended system should be in harmony with DCC. Finally, a computer program based on EXCEL software developed to analyze different DG systems to design optimum DG system for considered building.

In this paper the humidification–dehumidification desalination process is studied and its performance optimized using mathematical programming. An advantage of this method is consideration of the simultaneous effect of various parameters on process performance. An NLP system model is solved for three objective functions: minimization of specific thermal energy consumption, maximization of productivity and maximization of condenser heat recovery. The solutions have been improved especially from a productivity point of view in comparison with previous studies. The productivity objective function leads to the best solution if there is no limitation for the humidifier inlet water temperature. Otherwise, the specific energy objective function seems to be better than others. In the next step the effect of important parameters on optimum operation point of the HD process is analyzed and related variation curves are presented. Results reveal that the water to air mass flow rate ratio (L/G) is the most effective parameter and has an optimum value. Also the humidifier inlet water temperature will be an important parameter. The upper temperature results in more productivity and a smaller heat transfer area. But minimum specific thermal energy consumption of the system occurs at an optimum temperature. Finally, the analysis of the dehumidifier inlet water temperature shows that if the humidifier inlet water temperature is high, the specific thermal energy consumption can be decreased more through recycling of the outlet humidifier water into the dehumidifier.

In a previous paper, the performance of the humidification–dehumidification (HD) desalination process was optimized through mathematical programming. In this paper, by adding a solar system to the model, the total solar HD system is optimized. The main purpose of this optimization is the reduction of fresh water production costs. By using special operational and geographical constraints the model can be used for any region to determine optimum operation point of system. Results show that solution obtained by cost objective function has a cost 7–28% lower than other objective functions. Also recycling, in spite of the increase of productivity and decrease of specific thermal energy consumption of the HD process, increases the cost of production.

The present work is employed in two sections. Firstly the effect of different parameters such as pressure, temperature and anode and cathode channel depth on the performance of the proton exchange membrane (PEM) fuel cell was experimentally studied. The experimental result shows a good accuracy compared to other works.

The current targeting method for a heat exchanger network identifies an optimum point from material and energy balance prior to design. This optimum point is obtained by tradeoff between energy costs and heat exchangers capital cost. The current method does not consider piping costs associated to heat exchangers. In the targeting method as energy increases, heat exchanger area decreases, but piping size increases as the mass flow rate of utility streams increases. It means by increasing energy costs, heat exchanger cost decreases, however piping costs will increase. So piping costs can affect the total capital cost (heat exchanger costs plus piping costs). As a result considering piping costs in the targeting method may change the global optimality. It is obvious that without the correct initialization of design, the global optimum cost for heat exchanger network cannot be achieved.This paper presents a correlation for estimating piping costs for each stream as their piping size, material of construction and pressure rating. It also develops a method which the allows consideration of piping costs in the current targeting method.

This paper provides an exergy analysis for multistage cascade low temperature refrigeration systems used in olefin plants. The equations of exergy destruction and exergetic efficiency for the main system components such as heat exchangers, compressors and expansion valves are developed. The relations for total exergy destruction in the system and the system overall exergetic efficiency are obtained. Also, an expression for minimum work requirement for the refrigeration systems of olefin plants is developed. It shows that the minimum work depends only on the properties of incoming and outgoing process streams cooled or heated with refrigeration system and the ambient temperature. Using actual work input values, the exergetic efficiency of the low temperature cascade refrigeration system of a typical olefin plant is determined to be 30.88% indicating a great potential for improvements. The novelty of this paper includes the suggestions for increasing efficiencies, along with discussion about the reasons for deviation from reversible processes.

In this paper energy, exergy and thermoeconomic analysis of a combined cooling, heating and power (CCHP) system has been performed. Applying the first and second laws of thermodynamics and economic analysis, simultaneously, has made a powerful tool for the analysis of energy systems such as CCHP systems.

Thermoeconomic optimization of a typical 1000 MW Pressurized Water Reactor (PWR) nuclear power plant coupled to a Multi Effect Distillation (MED) desalination system with thermo-vapor compressor (TVC) is performed. A thermodynamic modeling based on the energy and exergy analysis is performed while economic modeling is developed based on the Total Revenue Requirement (TRR) method. The objective function based on the thermoeconomic analysis is obtained. The proposed cogeneration plant, for simultaneous production of power and fresh water, including sixteen decision variables is proposed for thermoeconomic optimization in which the goal is minimizing the cost of system product (including the cost of generated electricity and fresh water). The optimization process is performed using a stochastic/deterministic optimization approach namely as Genetic Algorithm. It is found that thermoeconomic optimization aims at reduction of sub-components total costs by reducing either the cost of inefficiency or the cost of owning the components, whichever is dominant. For some components such as evaporators, the improvement is obtained by reducing the owning cost of the sub-system at the cost of reduction of the thermodynamic efficiency. For components like as TVC + de-superheater, improvement is achieved by increasing the thermodynamic efficiency or decreasing the inefficiency cost.

A typical 1000 MW Pressurized Water Reactor (PWR) nuclear power plant coupled to a multi effect distillation desalination system with a thermo-vapor compressor (MED–TVC) is considered for optimization. The thermodynamic modeling is performed based on the energy and exergy analyses, while an economic model is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analyses are obtained. The proposed hybrid plant with ten decision variables for power plant and six decision variables for the desalination plant is optimized in a multi-objective optimization process. This approach is applied to minimize either the cost of the system product (including the cost of generated electricity and fresh water) and/or maximize the exergetic efficiency of the system. Three optimization scenarios including thermodynamic single objective, thermoeconomic single objective and multi-objective optimizations are performed using the Genetic Algorithm (GA). In multi-objective optimization, both thermodynamics and thermoeconomic objectives are considered, simultaneously. A series of optimum solutions namely Pareto frontier is obtained. In the case of multi-objective optimization, an example of decision-making process for selection of the final optimal solution from the available optimal points on the Pareto frontier is introduced. The results obtained using the various optimization scenarios are compared and discussed.► A typical nuclear power plant coupled to a desalination system is optimized. ► Objective functions based on thermodynamic and thermoeconomic analysis are obtained. ► The cost of the system product and exergetic efficiency of the system are optimized. ► A decision-making process for selection of the final optimal solution is introduced. ► Results obtained using various optimization scenarios are compared and discussed.

Understanding the dynamic behavior of distillation columns has received considerable attention due to the fact that distillation is one of the most widely used unit operations in chemical process industries. Heat integrated distillation sequences can provide significant energy savings; however, they exhibit a complex structure which appears to affect their controllability properties. One potential solution to this problem has been suggested through the operation of heat integrated sequences under the conditions that do not provide minimum energy requirements. But, this work proved that the process under the conditions of minimum energy requirements can operate with the best controllability properties. In this work, the controllability properties were analyzed with the application of the Morari resiliency index, condition number and relative gain array, which were frequency-dependent indices. The results showed that the controllability of the best heat integrated distillation column with the minimum total annual cost was better than other sequences.► We analyze the dynamic behavior of heat integrated distillation sequences. ► The control analysis properties are analyzed with the application of frequency-dependant indices. ► The results show that the controllability of best heat integrated distillation column is better than other sequences.

This paper describes the application of process integration principles to the design of oil refineries hydrogen network. In this regard, a design hierarchy as well as heuristics and required guidelines are proposed. The recommended rules compensate lack of procedure to the design and make the design process easier. The guiding principles of the design are based upon pinch technology and extending the heat integration concepts to mass integration. This research makes a designer able to maximise the amount of hydrogen recovered across the site during the design. Besides, it provides an opportunity for refineries to make most efficient use of hydrogen. The study is illustrated with an industrial case study. The work stages such as targeting, simulation etc. are performed in the REFOPT software environment. It is finally shown that this approach can design a network, which saves the total cost by $ 6.3459 million per year.