Abstract:
The rate of decaying of the energy resources with the passage of time is increases and may result
in reduction of panic reserves. Efficiency of all resources is crucial both in an environmental and
economic sense. Improper use of energy results in waste generation. It has environmental
impacts with regional, local and global implications. The key object is to adopt energy
management in every field in order to reduce the wastage of energy sources and cost
effectiveness without affecting productivity and growth. In a cement plant, nearly 30% heat is
lost, primarily from the pre-heater and cooler waste gases. This thermal energy can be tapped by
installing a Waste Heat Recovery Power Plant (WHRPP). Prediction of power plant output based
on operational parameters is major focus now days. This research present four different types of
machine algorithms. These algorithms include feed forwards neural network trained with particle
swarm optimization (PSO), simulated annealing, hill Climbing and genetic algorithm. These
algorithms takes steam inlet pressure (Mpa), steam inlet temperature (˚C) , steam flow (t/h) ,
exhaust temperature (˚C) as an input parameters to feed forward neural network to predict
hourly output of the power plant. The mean square error (MSE) of Particle swarm optimization is
better then the other three for the same number of iterations. The mean square error (MSE) of
Particle swarm optimization (PSO) for training is 0.0074034 and 0.0097715 for testing data,
Particle swarm optimization (PSO) shows promising results to predict power plant output.
