Optimization of mechanical draft wet cooling tower: an analysis towards performance enhancement

Abstract

This thesis is aimed at the optimization and control of the mechanical draft cooling towers considering diverse operating and environmental parameters. The proposed optimization methodologies are generalized which can be easily implemented using both experimental and theoretical methods. Different parameters related to energy, exergy and combined energyexergy performance of the cooling tower have been considered in various case studies, which are implicitly associated with the cost of the tower. The studies include the multi-objective optimization, the inverse optimization and the modified constrained-inverse optimization of mechanical draft cooling towers. The evolutionary algorithm-based optimization techniques such as unconstrained Genetic Algorithm (GA), Augmented Lagrangian Genetic Algorithm (ALGA) and elitist Non- dominated Sorting Genetic Algorithm (NSGA-II) have been used in various optimization studies. In order to predict the performance of the cooling tower under all inlet parameters, a new theoretical prediction model has been formulated that simultaneously estimates the water and air conditions at their respective outlets. The influence of all inlet parameters on the exergy performance of the cooling tower has been also investigated to quantify the effect of each input parameter.

Based upon the gaps identified in the literature survey, this research work commenced with the multi-objective optimization of forced and induced draft the cooling towers using the experiment-based forward methods. The multi-objective problem has been formulated for the fill/packing selection (out of trickle, film and splash fills) in the forced draft cooling tower based on the optimized performance. The experiments have been carried out using three fills in the lab-scale forced draft cooling tower. The relevant third order empirical correlations for key performance parameters such as the range, the tower characteristic ratio (Merkel number), the effectiveness and the water evaporation rate have been developed keeping water and air flow rates as design variables. The multi-objective optimization problem has been solved using NSGA-II and the optimal solution has been obtained using newly proposed Decision Making Score (DMS) based criterion. Based upon the highest DMS, the trickle (wire-mesh type) fill has been identified as preferred fill under the given set of operating conditions. Extending the scope of the above study, a multi-objective optimization based methodology has been established for the fan speed control in the induced draft cooling towers yielding optimum performance. The experiments have been performed on the labscale experimental setup of induced draft cooling tower and new empirical correlations for range, approach, Merkel number, effectiveness, and evaporation rate have been formulated. Using the maximal approach strategy, the simultaneous performance and energy optimization of the cooling tower has been carried out to retrieve the optimal values of air flow rate against the variation of water flow rate.

Next, considering the issue of energy saving, the controlling of the cooling tower has been aimed using the idea of inverse optimization. For the applications such as Organic Rankine Cycle (ORC), diesel engine power generation and Heating Ventilation and Air Conditioning (HVAC), the optimal control model has been developed. The experimental setup of forced draft cooling tower has been used to develop empirical correlations of important performance parameters keeping water to air ratio and water temperature as design variables. The inverse methodology implementing unconstrained GA has been used to satisfy the desired requirement of outlet water temperature for various applications. The study provides multiple combinations of water and air flow rates to satisfy the retrieved water to air mass flow rate ratio. Avoiding the multiple combinations issue with the traditional unconstrained inverse techniques, a new constrained optimization-based inverse methodology has been proposed in this study. This improved technique utilizes the objective function of energy or power consumption by the cooling tower. The desired requirement of given range, approach or cooling tower heat load has been taken care by the means of a constraint. The ALGA has been used to solve the problem and even for the multiple runs of the algorithm, a unique combination of mass flow rate of water and air has been obtained. The simulation based comparison of unconstrained and constrained inverse has been carried out to assess the merits of the improved inverse optimization technique. Further, the influence of the height on the optimal results has been also studied using three different tower heights (i.e., 0.62 m, 0.915 m and 1.2 m). After the experimental reconstruction of the retrieved optimal results, the model has been stated satisfactory for cooling tower control. The maximum error of 3.68% in achieved and the retrieved heat loads has been observed from the experimental reconstruction. Moreover, the air flow rate is found to be a critical parameter in energy-based cooling tower control.

The above studies are based on the experiment-based forward methods considering fixed environmental conditions. However, considering the challenge of capturing the environmental variations in the experiment-based optimization analysis, a new performance prediction model has been proposed for the simultaneous estimation of water and air outlet conditions. Using the prediction model, the quantification of all inlet parameters such as air dry-bulb temperature, relative humidity, water temperature, water mass flow rate and air mass flow rate affecting the exergy-related performance of the cooling tower has been made. In order to reduce the computational cost, the Taguchi and ANalysis Of VAriance (ANOVA) techniques have been implemented. The effects of all inlet parameters on exergy-related parameters such as convective exergy change, evaporative exergy change, net air energy change, water exergy change, exergy destruction and second law efficiency have been critically investigated in the present work.

Next, accounting the effect of environmental parameters such as dry-bulb temperature and relative humidity, the exergy destruction minimization-based constrained inverse technique satisfying the system heat load has been formulated. The study has been aimed at the synchronization of the cooling tower with the Hybrid Ground Source Heat Pump (HGSHP) and HVAC systems. The ALGA has been used to solve the problem in order to optimize the water temperature difference or the tower range, water and air mass flow rates. Considering the cooling dominated system operations, the optimal operating curves involving variation of controlling parameters under varying heat load have been proposed. Furthermore, the optimal operating maps to control the cooling tower under varying dry-bulb temperature and relative humidity have been established. The comparison of energy, exergy and combined energyexergy-based optimization methodologies under varying environmental parameters has been also conducted. The exergy and the combined exergy-energy based optimization techniques are observed to provide identical results across the whole range of the heat loads. However, energy-based optimization keeps the water flow rate at the minimum possible level, whereas, after attaining the maximum inlet water temperature, all three methods yield the same results.