This study analyzed waste heat of two sections including the rolling section and electric arc furnace with low and medium temperature ranges, respectively. Organic Rankine cycles (ORCs) and Kalina cycles are the best technologies for the conversion of low-quality and medium-quality thermal energy to electrical power. The ORC applies the principle of the steam Rankine cycle, but it uses organic working fluids with low boiling points to recover heat from lower temperature heat sources. Also, in the Kalina cycle, ammonia water is selected as the working fluid because of its variable boiling point and thermodynamic properties. This study employs the thermo-economic method using the genetic algorithm to optimize the performance of three different ORC systems including a basic ORC (BORC) system, a single-stage regenerative ORC (SRORC) system, and a double-stage regenerative ORC (DRORC) system using five different working fluids and a basic Kalina cycle with KCS34 and complex cycle under the same waste heat conditions. Based on the energy and exergy analysis, the complex Kalina cycle shows the best performance among all studied cycles. The next best performance was exhibited by KCS34 and DROC, respectively. In general, Kalina cycles and ORCs are suitable for low-temperature and medium-temperature heat sources, respectively. According to the thermo-economic analysis, KCS34 in the rolling section and DRORC in EAF show optimum performance for heat recovery. R11 and R113 are selected as the best working fluids for ORCs, and ammonia with a concentration of 0.9 in the mixture is the optimal solution for Kalina cycles.

Concerns over the environmental impacts of GHG emissions have led refineries to deploy different FGR methods, most of which requiring new equipment and costly design and construction. In this research, we study three main methods of FGR to reduce energy consumption and control environmental pollution by the use of an environmental flow diagram and HYSYS, Thermo flow and aspen software. Three main scenarios are investigated for FGR with a technical and economic assessment of energy consumption and total flare gas flow is 4.5 MMSCFD in a gas field in southern Iran. The first scenario is pressurizing and injecting flare gas into oil wells, the second is producing electricity and injecting surplus flare gas into oil wells, and the third is producing power by using a combined heat and power system (CHP) and an internal combustion engine. Flare gas pressurizing and injection to oil wells with an internal investment rate of 171% and a payback period of 1.02 years is one of the best methods to reduce gas flare. In addition, all soil, air, and water pollution quantities are investigated together. After determining environmental indicators and collecting data an Environmental Flow Diagram (EFD) is developed to illustrate contamination sources and points and the relationship of units with respect to environmental issues. In these fields, the amount of CO₂, CO, NO_X in the furnaces, dehumidifier and flare were decreased by 100%, 100%, and about 57% using EFD and according to the results for the collection of associate gases, respectively. Abbreviations and symbols EFD: environmental flow diagram; GTE: gas to ethylene; PFD: process flow diagram; IRR: internal rate of return; RES: reference energy system; LPG: liquefy petroleum gas; FGR: flare gas recovery; CHP: combined heat and power; GHG: greenhouse gas; NPV: net present value; UGS: underground gas storage; PBP: payback period; GTL: gas to liquid: RVP: reid vapor pressure; APG: associated petroleum gas; BOD: biochemical oxygen demand; MCDM: multiple criteria decision-making; COD: chemical oxygen demand; GTG: gas turbine generator; EOR: enhanced oil recovery; MMSCFD: million standard cubic feet per day; CO_2: carbon dioxide; CO: carbon monoxide; MW: megawatt; kWh: kilowatt-hour; barg: gauge pressure; kW: kilowatt; MMm^3: million cubic meter.

A model for operating an energy hub-based multiple energy generation micro-grid is optimized using the demand response program. The optimized objective model is validated against energy demand of a residential building in Tehran, Iran. The mathematical model and optimal analysis of the proposed tri-generation micro-grid are implemented by using a real-world modelling and considering the constraints of the storage system, demand response program and the performance of the devices and the power and gas grids. The dynamic optimal operation model is prepared on the basis of the mixed integer linear programming on the subsequent day and is solved to minimize the costs of energy supply. To demonstrate the improvements, different scenarios are developed so that the renewable energy resources and storages are fed into the combined cool, heat and power system gradually. The results reveal that the inclusion of each element results in a significant improvement in the operational parameters of the micro energy grid. Scenario 1 includes a combined cool, heat and power system alone, Scenario 2 is supplemented with renewable wind and solar energy resources in addition to combined cool, heat and power system and Scenario 3 includes electrical, heat and cold storages in addition to combined cool, heat and power system and renewable energy sources. Scenario 4 is similar to Scenario 3 in terms of equipment, but the only difference lies in the use of the demand response program in the former. Total operational cost is 12.7% lower in Scenario 2 than in Scenario 1, 9.2% lower in Scenario 3 than in Scenario 2 and 8.6% lower in Scenario 4 than in Scenario 3. Practical application: An optimized operation method is prepared for combined cool, heat and power systems running in different operation modes in which renewable energy sources and storages are added to the combined cool, heat and power and the demand response program is applied. The results reveal that the cost of energy supply, including the cost of electricity, gas and pollutant emissions, is reduced and the qualitative parameters of the operation, including efficiency and reliability of building micro-grid, are increased. The proposed algorithm and the evaluation method will enable building operators to plan demand response activity on the residential building in Tehran, while this can be extended to other buildings too.

The main purpose of this paper is to improve optimal design and analysis of Combined Cooling, Heating and Power production (CCHP) system for an office building of Qom University based on hybridization of solar energy and gas turbine cycle. The proposed system is an integration of a gas turbine, a solar plate collector, a heat recovery steam generator, a steam turbine, and absorption chiller. To calculate solar energy dynamically, the transient simulation has been considered. In this regard, thermodynamic modeling and dynamic simulation of hybrid Solar-CCHP have been performed in TRNSYS software. Furthermore, energetic, exergetic, economic, exergoeconomic, and exergoenvironmental modeling and analysis have been performed in different modes dynamically. So, the computer code has been developed to perform these analyses. In addition, multi-objective optimization through NSGA-II with maximum exergy efficiency, minimal total exergy cost and minimal exergoenvironmental impacts have been performed simultaneously. The results show that the three objective parameters have been improved in cooling mode compared to base system at 3%, 15%, and 10%, respectively, and in heating mode at 11%, 12%, and 32%, respectively.

With the global production of 150 million tons in 2016, ethylene is one of the most significant building blocks in today’s chemical industry. Most ethylene is now produced in cracking furnaces by thermal cracking of fossil feedstocks with steam. This process consumes around 8% of the main energy used in the petrochemical industry, making it the single most energy-intensive process in the chemical industry. This paper studies a tubular thermal cracking reactor fed by propane and the molecular mechanism of the reaction within the reactor. After developing the reaction model, the existing issues, such as the reaction, flow, momentum, and energy, were resolved by applying heat to the outer tube wall. After solving the entropy generation equations, the entropy generation ratio of the sources was evaluated. The temperature of the tube/reactor was tuned following the reference results, and processes were replicated for different states. The verification of the modeling and simulation results was compared with the industrial case. The Genetic Programming (GP) machine learning approach was employed to generate objective functions based on key decision variables to reduce the computational time of the optimization algorithm. For the first time, this study has proposed a systematic approach for optimizing a thermal cracking reactor based on a combination of Genetic Programming (GP), Water Cycle Algorithm (WCA), and Genetic Algorithm (GA). In this regard, multiobjective optimization was performed based on the maximization of the products and entropy generation with the generation of GP objective functions. The key decision variables in this study included inlet gas temperature, inlet gas pressure, air mass flow rate, and wall temperature. The results showed that the weighted percentage of products after optimization increased to 61.13% and the entropy production rate of the system decreased to 899.80 J/s, displaying an improvement of 0.85 and 16.51% compared with the base case, respectively, with the multiobjective GA algorithm. In addition, by applying the multiobjective WCA, the weighted percentage of products increased to 61.81%. The entropy production rate of the system decreased to 882.72 J/s. So, an improvement of 1.97% in weights of products and an improvement of 18.77% in entropy generation have been achieved compared with the base case.