Abstract
Pump-as-turbine (PAT) technology permits two operating states—as a pump or turbine, depending on the demand. Nevertheless, designing the geometrical components to suit these operating states has been an unending design issue, because of the multi-conditions for the PAT technology that must be attained to enhance the hydraulic performance. Also, PAT has been known to have a narrow operating range and operates poorly at off-design conditions, due to the lack of flow control device and poor geometrical designs. Therefore, for the PAT to have a wider operating range and operate effectively at off-design conditions, the geometric parameters need to be optimized. Since it is practically impossible to optimize more than one objective function at the same time, a suitable surrogate model is needed to mimic the objective functions for it to be solvable. In this study, the Latin hypercube sampling method was used to obtain the objective function values, the Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Generalized Regression Neural Network (GRNN) were used as surrogate models to approximate the objective functions in the design space. Then, a suitable surrogate model was chosen for the optimization. The Pareto-optimal solutions were obtained by using the Pareto-based genetic algorithm (PBGA). To evaluate the results of the optimization, three representative Pareto-optimal points were selected and analyzed. Compared to the baseline model, the Pareto-optimal points showed a great improvement in the objective functions. After optimization, the geometry of the impeller was redesigned to suit the operating conditions of PAT. The findings show that the efficiencies of the optimized design variables of PAT were enhanced by 23.7%, 11.5%, and 10.4% at part load, design point, and under overload flow conditions, respectively. Moreover, the results also indicated that the chosen design variables (b2, β2, β1, and z) had a substantial impact on the objective functions, justifying the feasibility of the optimization method employed in this study.
Highlights
The generation of electricity presents numerous problems, which have been addressed through the years by several methods to reduce its operational costs and environmental effects
The findings indicated that the Artificial Neural Network (ANN) model was apt for assessing the performance of PAT at different heads and flow rates
Multiple Linear Regression (MLR) is an approach used to explain how a dependent variable is described by several independent variables [49]
Summary
The generation of electricity presents numerous problems, which have been addressed through the years by several methods to reduce its operational costs and environmental effects. The production of electricity through fossil fuels poses high effects on the ozone layer by depletion. This depletion of the ozone layer causes global warming, which is detrimental to aquatic life and ecosystems at large [1]. Renewable energy such as the solar power, biofuels, and hydropower can be a suitable solution for ecological problems. Of all the green energy sources, hydropower provides a suitable means of energy production. Hydropower is energy generated through hydraulic turbines from water sources like the ocean, rivers, and waterfalls. Pump-as-turbine (PAT), alternatively, is a simple hydraulic machine that can be used to generate electricity using hydropower systems. The PAT can be used simultaneously as a pump and turbine without any major geometrical parameter change [3]
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