Post harvest crop processing machine

 Chaff is hay cut into small pieces for feeding to livestock.  Chaff can be carried by manually operated machine and electricity operated machine.  This paper presents experimental work executed for establishing mathematical model and simulation for chaff cutting operation establishment of mathematical model and its optimization.  Has been established for responses of the system such as instantaneous resistive torque (πD1), number of cuts (πD2) and process time (πD3).  Model for dependent term instantaneous resistive torque: πD1.  The models are:  Tc = 1.645×103 (π1)3.8074 (π2)0.5141 (π3)-0.4521 (π4)1.686 (π5)2.3237 (π6)0.8162 (π7)-0.4189 (π8)-0.3840.  Model for dependent term number of cut (C) πD2: πD2= 0.6449 (π1) 0.0001 (π2)-0.0146   (π3)0.3471 (π4) 1.0151 (π5)0.2781 (π6)0.1233 (π7)0.9701 (π8)0.4773.  Model for the dependent term process time, πD3: πD3 = 43.43 (π1)0.0001 (π2) 0.1753 (π3) 0.0012 (π4) 0.0001 (π5) 0.0505 (π6) 0.2508 (π7)1.0008 (π8) 0.0004.  This paper discusses about the applications for pedal power technology. Keywords: manually energized flywheel motor, spiral jaw clutch, fodder &nbsp

Modeling of Biodiesel Plant Design: Data Estimation and Generation Based on Suppositions and Interpolation

This paper presents the approach for the Biodiesel plant design data estimation and generation to support the mathematical formulation of the model. Presented approach is based on certain suppositions. Design data is estimated by using actual fundamentals involved in the design of the resources and equipments. Later, the sample space is increased by generating the design data. Design data is generated using the concept of linear interpolation, where the basic data fitting model is developed and then the intermediate design data values are obtained to increase the sample space. This facilitates the formulation of mathematical model. Experimental results are obtained through the MATLAB implementation

Field Data-based Mathematical Simulation Of Manual Rebar Cutting

Construction process activities are very complex in nature and there have been attempts to simulate them via numerous methods. Manual work, which constitutes a large proportion of total construction in India and developing countries, requires emphasis. Field data-based mathematical simulations develop an empirical relation between inputs and outputs; once the model is developed and weaknesses have been identified, methods can be easily improved and optimised for output goals. This paper covers in detail the process of developing models for the rebar cutting subactivity of reinforced concrete construction in residential buildings. These models are evaluated using sensitivity analysis, optimisation techniques and reliability analysis and are validated using artificial neural networks

DESIGN AND DEVELOPMENT OF A ALTERNATE MECHANISM FOR STONE CRUSHER USING RELATIVE VELOCITY METHOD

In this paper alternate mechanism for design and analysis of small size stone crusher mechanism is proposed. The basic idea is to optimize the design of the crusher which would be best suited for stone which need crushing force of 3 Tons. Presently for reducing sizes of stones from 10cm x 10cm to 2.5cm x 2.5cm in quarries is laborious job and is done manually our approach is to design a best optimum mechanism for said conditions

FORMULATION OF MATHEMATICAL MODEL FOR MAINTENANCE COST OF A STONE CRUSHING PLANT BASED ON DIMENSIONAL ANALYSIS AND MULTIPLE REGRESSION

This paper presents the approach for the mathematical modeling of maintenance cost for the set up of new Stone Crushing Plant based on the dimensional analysis and multiple regression. Presented maintenance cost mathematical model is derived based on the generated design data. Design data is generated from actual design of all stone crushing plants followed by static and dynamic analysis. Estimation of design data is carried out based on the assumed plant layout. Dimensional analysis is used to make the independent and dependent variables dimensionless and to get dimensionless equation. Later, multiple regression analysis is applied to this dimensionless equation to obtain the index values based on the least square method. The mathematical model of maintenance cost is formulated using these obtained index values. Finally, the formulated model is evaluated on the basis of correlation and root mean square error between the computed values by model and the estimated values

FORMULATION OF MATHEMATICAL MODEL FOR PRODUCTION TURNOVER OF BIODIESEL PLANT BASED ON DIMENSIONAL ANALYSIS AND MULTIPLE REGRESSION

This paper presents the approach for the mathematical modeling of production turnover for the set up of new Biodiesel plant based on the dimensional analysis and multiple regression. Presented production turnover mathematical model is derived based on the generated design data. Design data is generated from the estimated design data. Estimation of design data is carried out based on the assumed plant layouts of different capacities. Dimensional analysis is used to make the independent and dependent variables dimensionless and to get dimensionless equation. Later, multiple regression analysis is applied to this dimensionless equation to obtain the index values based on the least square method. The mathematical model of production turnover is formulated using these obtained index values. Finally, the formulated model is evaluated on the basis of correlation and root mean square error between the computed values by model and the estimated values

FORMULATION OF MATHEMATICAL MODEL FOR PRODUCTION TURNOVER OF BIODIESEL PLANT BASED ON DIMENSIONAL ANALYSIS AND MULTIPLE REGRESSION

This paper presents the approach for the mathematical modeling of production turnover for the set up of new Biodiesel plant based on the dimensional analysis and multiple regression. Presented production turnover mathematical model is derived based on the generated design data. Design data is generated from the estimated design data. Estimation of design data is carried out based on the assumed plant layouts of different capacities. Dimensional analysis is used to make the independent and dependent variables dimensionless and to get dimensionless equation. Later, multiple regression analysis is applied to this dimensionless equation to obtain the index values based on the least square method. The mathematical model of production turnover is formulated using these obtained index values. Finally, the formulated model is evaluated on the basis of correlation and root mean square error between the computed values by model and the estimated values