Problem statement: Waste Heat Recovery (WHR) steam Technology is a proven Technology pioneered by Japanese for cement plant and it is economically viable. Electrical Power can be generated by adopting the latest technology in this field. It used a medium to low temperature (120-350°C) Turbine technology (standard thermal power plants run on steam temp-500°C). Approach: It requires treat exchangers (Hx) designed for high dust load, no additional fuel is required Kymore Cement Works has proposed to install a power plant of 9 MW which will be operated with the recovered waste heat from the clinker coolers and kilns from its both clinker units. The hot air from cooler and kiln passes through the ESP is taken to the waste heat recovery exchangers. Adequate size of heat-exchangers will be located at proper locations in order to achieve optimum temperature of Thermic Oil from waste gases. Conclusion/Recommendations: This is required for optimal power yield. The hot flue gases will pass through a Heat Exchanger by which the temperature (heat of the waste gas is transferred to the internal elements of the heat exchangers which is used for heating of the thermo oil. In turn this thermal oil vaporizes the organic fluid in close loop cycle. Multi level pressure turbine system will be installed which increases usable heat content resulting in higher power output. The turbine will be run by the organic vapors to generate the electrical energy. The system of oil collection, oil transfer to the vaporizer and its recycling process will be made for the complete recycling of the thermal oil

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The computer numerical control (CNC) machines are chiefly used for the production of jobs with high accuracy and high speed. The CNC machining centers perform the machining operations according to the given program instructions which are commonly programmed by a CNC programmer. In this paper, a procedure to develop an automatic CNC program for machining of different types of holes by using different machine learning algorithms is developed. The machine learning algorithms namely support vector machine (SVM) and restricted boltzmann machine algorithm (RBM) with deep belief network (DBN) are used for the automatic development of CNC machining programs of different types of holes. Initially, the position and other parameters of machining operations are identified and thereafter the CNC machining program is developed by using the MATLAB application. The automatically developed CNC programs are tested on a CNC simulator. It is found that the application of RBM machine learning algorithm with DBN outperforms the SVM machine learning algorithm for the development of automatic CNC machining program for the machining of blind holes, through holes, counterbores and countersink operations.

The real-time scheduling of automatic guided vehicles (AGVs) in flexible manufacturing system (FMS) is observed to be highly critical and complex due to the dynamic variations of production requirements such as an imbalance of AGVs loading, the high travel time of AGVs, variation in jobs, and AGV routes to name a few. The output from FMS considerably depends on the efficient scheduling of AGVs in the FMS. The multi-objective scheduling decisions for AGVs by nature inspired algorithms yield a considerable reduction throughput time in the FMS. In this paper, investigations are carried out for the multi-objective scheduling of AGVs to simultaneously balance the workload of AGVs and to minimize the travel time of AGVs in the FMS. The multi-objective scheduling is carried out by the application of nature-inspired grey wolf optimization algorithm (GWO) to yield a balanced work-load for AGVs and also to minimize the travel time of AGVs simultaneously in the FMS. The output yield of the GWO algorithm is compared with the results of benchmark problems from the literature. The resulting yield of the proposed algorithm for the multi-objective scheduling of AGVs is observed to outperform the existing algorithms for scheduling of AGVs.

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