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Showing 4 results for Optimization

Asghar Lashanizadegan, Mahmoodreza Rahimi, Hadie Mazlumi,
Volume 1, Issue 1 (9-2017)
Abstract

Furnaces in refinery and petrochemical processes are major consumers of energy. The most important factors for the controlling the energy consumption of the furnace can be divided into three main groups The first group includes the potential savings without cost or low cost, such as adjusting air – fuel ratio in the burner and pressure control into the furnace, The second group includes the potential savings with medium cost such as insulating body, and the third group includes the potential savings with high investment such as heat recovery from the exhaust flue. In this paper, the thermal energy savings potential on the 4 fixed- furnaces in the Loabiran companies are investigated and calculated that savings potential of adjusting air – fuel ratio in the burner is 165,973,500 Rials in the year, controlling pressure inside the furnace 95,822,300 Rials in the year, body insulation 622,167,700 Rials and recycled flue gases 929,762,400 Rials in the year. Also Loabiran companies uses regenerator that is a periodic heat recovery system to preheat the incoming air. This will result in annual savings for the daily production of 20 tons of frit, are 3,577,000,000 Rials.
Rasoul Rajabpour , Bahram Sami Kashkoli , Tahereh Faraji , Abolghasem Mohamadzadeh , Seyfollah Amin,
Volume 1, Issue 2 (1-2018)
Abstract

 

In this paper, the optimal operation of pumping stations was determined using a genetic algorithm so that the minimum energy cost. The schedule for the operation of the water pump system can be a significant savings in the cost of energy to be achieved. Determine the optimum pump operation schedule an optimization model - simulation-based genetic algorithm was developed. The model integrates GA optimizer and EPANET hydraulic network solver in MATLAB. The proposed model is applied to find the optimal pump operation schedule of Dogonbadan water conveyance system from Kowsar Dam in an ordinary day of the year. The comparison of optimal schedule with ordinary operation strategy shows 26.8 percent reduction in total energy cost. This indicates the high capability of the proposed model.
 

 
Mrs Hajar Bagheri Tolabi, Dr M.r. Shakarami, Dr E. Rok Rok,
Volume 2, Issue 1 (2-2017)
Abstract

This paper presents a new hybrid method for optimal multi-objective reconfiguration simultaneous determining the optimal size and location of Distributed Generation (DG) in a distribution feeder. The purposes of this research are reducing the losses, improving the voltage profile and equalizing the feeder load balancing in a distribution system. Ant Colony Optimization (ACO) approach as a Swarm Intelligence (SI) based algorithm is used to simultaneously reconfigure and identify the optimal capacity and location for installation of DG units in the distribution network. In order to facilitate the algorithm for multi-objective search ability, the optimization problem is formulated for minimizing fuzzy performance indices. The multi-objective optimization problem is transformed into a fuzzy inference system (FIS), where each objective function is quantified into a set of fuzzy objectives selected by fuzzy membership functions. The proposed method is validated using the IEEE 33 bus test system at nominal load. The obtained results prove this combined technique is more accurate and has an efficient convergence property compared to other intelligent search algorithms. Also, the obtained results lead to the conclusion that multi-objective reconfiguration along with placement of DGs can be more beneficial than separate single-objective optimization.


Sedigheh Janipour, Samad Nejatian, Mosayeb Bornapour,
Volume 2, Issue 2 (3-2017)
Abstract

Distribution feeder reconfiguration for loss reduction is a very important way to save the electrical energy. This paper proposes a new hybrid evolutionary algorithm to solve the Distribution Feeder Reconfiguration problem (DFR) .The algorithm is based on combination of a New Fuzzy Adaptive Particle Swarm Optimization (NFAPSO) and differential evolution algorithm (DE) called NFAPSO-DE. To exploit the advantages of the exploration ability of DE and the high speed search system and the ability to control and adjust the parameters of PSO algorithm, a hybrid PSO-DE method is proposed. The hybrid method uses the PSO to find the region of optimal solution, and then a combination of PSO and DE to find the optimal solution. In other hand, due to the results of PSO algorithm highly depends on the values of their parameters such as the inertia weight and learning factors, a fuzzy system is employed to adaptively adjust the parameters during the search process. Finally, the proposed algorithm is tested on 33 bus and 69 bus distribution test systems. The results of simulation shows that the proposed method is very powerful and effective to obtain the global optimization.



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