| dc.description.abstract | Water technology plays a crucial role in fulfilling the need for clean water access
and efficient water management, especially in rural areas. As an alternative to
water technology that requires sustainable electrical energy, the use of hydram
pumps is considered quite effective because it can lift water without using electrical
energy. Response Surface Methodology (RSM) and Taguchi method are different
statistical approaches in experiment design and process optimisation. Statistical
analysis is key in understanding the complexity of the relationship between design
variables and hydram pump performance. The results show that RSM and Taguchi
methods provide different number of experiments, optimal points, equation models,
ANOVA and optimal variables in variable settings to increase hydram pump
discharge. In the RSM method there are 20 experiments, with the optimal point at
3 m input height, 12 m input length, and 120 cm vacuum tube height, while for the
Taguchi method there are 9 experimental variations and the optimal point is
obtained at 3 m input height, 12 m input length, and 120 cm vacuum tube height.
For the equation model, the RSM method shows a complex mathematical equation
involving interactions between variables, while the Taguchi method provides a
simpler equation. As for the most optimal variable when viewed from the Significant
value in both methods in the ANOVA table, it is found that the input height variable
is the most significant variable in optimising the response (discharge). A better
understanding of these two methods can help the selection of appropriate methods
for specific situations, strengthen the understanding of hydram pump performance,
and contribute to the development of more efficient and sustainable hydram pump
technology. | en_US |
