P. K. Boruah

Prediction of Moisture Loss in Withering Process of Tea Manufacturing Using Artificial Neural Network

The first and foremost process in tea manufacturing, withering, is the foundation for producing good quality. Moisture plays an important role in the manufacturing process of tea to get the desired quality. In this paper, a novel in situ instrumentation technique is proposed and validated experimentally for prediction of moisture loss (ML) in the withering process. In the proposed technique, ML is predicted based on the inlet and the outlet relative humidity (RH) and temperature during the process of withering. Network capable smart sensor nodes are developed for the measurement of RH and temperature at the inlet and outlet of the withering trough. Architecture of the nodes and network is described. A scaled-down prototype of an enclosed trough is developed to perform withering of tea leaves. Based on the data measured by the system, ML is predicted by using artificial neural network. Nonlinear autoregressive model with exogenous inputs is used for predicting the ML. The predicted ML is compared with the actual amount of ML measured by weight loss. A total of nine experiments are conducted for nine batches of tea leaves. The data collection, their analysis and results are reported in this paper. The observed result shows a good agreement between the predicted and actual ML. The maximum mean error in prediction is −3.6%.

Design of a Small Cosmic Ray Air Shower Array to Study Atmospheric Effects

High energy primary cosmic rays (\(E > 100\) TeV) produce showers of secondary particles in the atmosphere, called EAS (Extensive Air Showers). An EAS may be recorded by the method of coincidence between a number of scintillation detectors. A small square array of four detectors is designed using Monte Carlo simulation method combined with analytical lateral distribution function for the charged particles at ground level. Preliminary calculation using CORSIKA Simulation code gives an estimate of the size of the array as a function of detector effective area and threshold energy of the primary cosmic ray initiating the shower. The growth and absorption of an EAS depends on the amount of matter traversed, which is more for higher zenith angles. Hence zenith angle distribution may be correlated with atmospheric effects. Experimentally zenith and azimuth angles for individual events can be measured by timing information recorded by each scintillator. In this paper, design of the array in terms of size and threshold parameters and Data Acquisition methods are presented.

An ANN Model to Estimate the Impact of Tea Process Parameters on Tea Quality

Present study deals with the development of an artificial neural network (ANN)-based technique for tea quality quantification by monitoring fermentation and drying condition of the tea processing stages. An RS485 network-based instrumentation system has been developed and implemented for data collection for these two stages. Three calibrated sensor nodes are installed in the fermentation room due to its larger floor area to collect temperature and relative humidity (RH). Dryer inlet temperature is recorded using a calibrated thermocouple-based sensor node. From seven input parameters and target quality data obtained from tea taster, the ANN model has been developed to find the correlation between the process condition and the tea quality. From the correlation study, more than 90% classification rate is obtained from the model. The model is also validated with some independent data showing more than 60% correlation. Error in terms of root mean square error (RMSE) is about 0.17. This model will be helpful for improvement of tea quality.