Minimizing pump energy in a wastewater processing plant

Data-driven minimization of pump operating and maintenance cost

“…In Table 2, the average of MAPE and sdAPE of predicting energy consumption both are 0.02 which is the same as the results reported in Table 2 of Zhang et al (2012). However, the average of MAPE and sdAPE of predicting wastewater outflow rate are also both 0.02 which is much lower than such results, MAPE¼0.05 and sdAPE¼0.06, obtained according to Table 3 in Zhang et al (2012). Fig.…”

“…Due to heterogeneity of the pumps and head effect Baron et al, 2005), it is challenging to model all these operational configurations. Considering individual configurations may lead to more accurate models (Zhang and Kusiak, 2011;Zhang et al, 2012). The energy consumption and wastewater outflow of pump configurations were modeled independently in Zhang et al (2012), which has led to numerous models.…”

“…Considering individual configurations may lead to more accurate models (Zhang and Kusiak, 2011;Zhang et al, 2012). The energy consumption and wastewater outflow of pump configurations were modeled independently in Zhang et al (2012), which has led to numerous models. In this paper, a new data-driven modeling strategy is introduced and tested.…”

Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm

“…They proposed a procedure based on combination of ANN and artificial bees colony (ABC). Zhang et al [23] expressed the energy savings in wastewater processing plant pump operations via employing the artificial neural network and particle swarm optimization algorithm.…”

Systematic Optimization of the Design of Steam Cycles Using MINLP and Differential Evolution

“…Therefore, for large-scale or for very ill-conditioned and highly constrained problems, more robust and heuristic searching methods, such as genetic algorithms [27][28][29][30] and particle swarm optimization [31,32], have been applied and discussed for energysystems optimization or energy-resource scheduling and planning. Although very successful, these algorithms are still associated with great difficulties because, for example, the global optimum might correspond to a point and a very small variation in any of the continuous variables produces infeasibility.…”