Instrumentation Vibrational energy harvesters Predicting moisture loss in tea leaves
Vibrational energy harvesters

Dr Utpal Sarma and Babak Montazer report about works on Piezoelectric MEMS Energy Harvester Systems, which are published in IEEE Transactions on Instrumentation and Measurement and Journal of Circuits, Systems and Computers.

Utpal Sarma and Babk Montazer

Abstract #1
This work addresses an efficient way of conversion of ambient vibrational energy to electrical by structure optimisation of piezoelectric film. Polyvinylidene fluoride has been used for this study. The effects of shape variations of cantilever beam with multilayer configuration based on Euler–Bernoulli theorem without considering the proof mass attached at the free end has been investigated. Most of piezoelectric vibrational energy harvesters are designed considering the presence of proof mass and in this paper it has been tried to convey the idea, that with the new optimised shape, the cantilever itself can behave as a flexible proof mass for energy enhancement. The possibility of harvesting energy from three different geometries - (1) near edge width quadratic (NEWQ), (2) half quadratic (HQ), and (3) Trapezoidal have been presented. Scanning electron microscopy is used to measure the thickness of the various layers. The NEWQ design shows the best performance in terms of power and resonance frequency (fr < 200 Hz) compared with the others. Here, the study of the piezo-film models using finite element method simulation software (COMSOL Multiphysics optimization module) and experimental validation are also presented. Using the models, the generation of voltage/power has been analyzed under various excitation frequency and load resistance. Moreover, stress/strain distribution and the displacement have also been highlighted in this paper.

Abstract #2
Modelling and analysis of a MEMS piezoelectric (PZT-Lead Zirconate Titanate) unimorph cantilever with different substrates are presented in this work. Stainless steel and Silicon h110i are considered as substrate. The design is intended for energy harvesting from ambient vibrations. The cantilever model is based on Euler–Bernoulli beam theory. The generated voltage and power, the current density, resonance frequencies and tip displacement for different geometry (single layer and array structure) have been analysed. Variation of output power and resonant frequency for array structure with array elements connected in parallel have been studied. Strain distribution is studied for external vibrations with different frequencies. The geometry of the piezoelectric layer as well as the substrate has been optimised for maximum power output. The variation of generated power output with frequency and load has also been presented. Finally, several models are introduced and compared with traditional array MEMS energy harvester. 

Predicting the moisture loss in tea leaves

Dr Utpal Sarma and his co-researchers report about 
a novel in-situ instrumentation technique for prediction of moisture loss from tea leaves during the withering process, which is crucial in determining the tea quality. This work has been published in IEEE Transactions on Instrumentation and Measurement.

Nipan Das, Kunjalata Kalita, P K Baruah, and Utpal Sarma

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 work, 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%.