Posted at 05.10.2018
System Optimization
The main concern is to find the optimum value for every design parameter for each prediction period for a total simulation time of 12 hours. The simulation is performed on the decided on system predicated on the search engine optimization timeframe with an acceptable accuracy and the marketing process is requested a prediction period of one. The value of an individual design parameter and interior loads are set throughout a prediction period and may change from one prediction period to another.
Modeling the liquid desiccant system with the CC/DV system is intricate activity with multi-variables engaged, several equations are combined and indirect relationships between different guidelines are present. Since several non-linear equations are resolved, it is advised to employ a groundbreaking derivative free marketing tool that uses the direct search technique. The simplest optimization tool that might be used for the suggested case is the genetic algorithm marketing tool since it is derivative free, based on numerical analysis, which is somehow effective if weighed against other derivative established optimization schemes. Moreover, it fetches the global the least a particular function.
Our choice of using a derivative free algorithm to resolve the search engine optimization problem is integrated by the evolutionary hereditary algorithm. Hereditary algorithms are adaptive methods which may be used to resolve search and marketing problems, and derive from the genetic procedure for biological organisms. Genetic algorithms are growing more and more popular and increasing from simple design optimization to online process control. The power of the hereditary algorithm comes from its robustness, being acceptably good to find the near optimum solution and being relatively quick [1]. A competent optimization strategy uses two techniques to find the optimal solution, exploration and exploitation, and this is what genetic algorithm does.
For the optimized control strategy used for the chilled ceiling, displacement ventilation system the parameters of the chilled roof and displacement ventilation are assorted; this variation leads to a minimal optimum cost that results in the minimum amount cost that might be attained in the system.
Referring to the machine figure and considering the optimum control strategy, the factors which may be used for cost optimization are:
Equation Chapter 6 Section 3Each varying in the optimization routine has a lower and an higher bound. These bounds identify the interval where the genetic algorithm searches for the optimal cost and derive from physical factors. The bounds for the different variables according to ASHRAE's recommendations are:
There are several non-linear constraints that are applicable to the machine. These constraints are related to thermal comfort issues, condensation inside the area and physical constraints. The constraints may be redefined in the next list
The fitness function:
To be able to enhance the acceleration of the genetic algorithm, the electro-mechanical cost function and constraints are blended within a cost function by using penalty functions, thus the fitness cost function may be written as:
The coefficients , , , , and in these function are the weight factors for his or her related charges costs. The weight factors values are set in line with the system parameter. For the existing system, 's are set to unity.
The objective function that is usually to be optimized is the total operational cost of the machine; this cost may be split into:
Note that in this work the price is given in systems of KW.
The chiller is the key energy consuming part in our system. The chiller cost is indicated in terms of the part insert ratio. The part insert ratio is defined as the percentage of the current weight on the chiller divided by the look weight that the chiller could take care of. Mathematically, the part load ratio is found from the equation
The coefficient of performance of the chiller is correlated to the strain equation utilizing the following relationship:
The cost of the chiller is determined utilizing the following equation
The admirer cost is directly related to air mass circulation rate by using the following equation:
The pump cost relates to the pump brain, liquid desiccant mass stream rate, and the efficiency of the pump. The energy of the pump is assessed by multiplying the pressure difference by the volumetric movement rate and dividing the result by the pump efficiency; mathematically the pump cost equation may be written as
Note that the pump cost is not contained in the cost function, since the desiccant mass circulation rate is costant.
Therefore the total energy used can be expressed by the next equation:
The cost function for the constraints may be written such that they may be incorporated in to the online cost function in a straightforward manner. These constraints are related with their respective threshold prices in a way that when the constraints are violated, the fitness function could have a very large value.
The exponential term helps to penalize the cost function when-ever the thermal comfort and ease of occupants in the room TH lowers below the minimum established value THmin. This may improve the value of the cost function dramatically and the set of variables at hand is rejected. The integration of the constraint conditions within the target function manifestation and the utilization of the exponential form to regulate the constraints' cost were implemented by Keblawi et al. [13] and Hammoud et al. [4].