Studies On Strength Characteristics Of Soil Mixed With Bio-Waste

Researches are always in quest for finding innovative methods for ground improvement using sustainable and environmental friendly solutions. Theproduction of large quantity of biowastes all over the world faces serious problems of handling and disposal. Coir pith is a biowaste from coir industry and sugarcane baggase is another biowaste obtained after extractingjuice from sugar cane. So the present study is an investigation into the effect of coir pith and sugarcane baggase on some geotechnical properties of red earth. The investigation includes study on variation of properties such as O.M.C, maximum dry density, C.B.R. values,unconfined compressive strength and permeability when these materials are included in soil. Several conclusions are arrived at, on the basis of the experiments conducted and it may be helpful for predicting the behavior of such soil matrix

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems

Reinforcement Learning solution for Unit Commitment Problem through pursuit method

Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems