Showing papers by "Mani Bhushan published in 2022"

Plantwide Decentralized Controller Design for Hybrid Solar Thermal Power Plant

Abstract: Solar Thermal Power (STP) plants are promising avenues for solar energy assisted power generation. However, they face operational challenges due to diurnal and seasonal variations in available solar radiation, and varying atmospheric conditions in terms of cloud cover, dust levels, etc. Thus, to operate an STP plant at high efficiency and to meet the electricity demand, optimization and control strategies are critical. This paper focuses on designing decentralized controllers to ensure the safe and efficient operation of a hybrid STP which was designed and commissioned a few years ago (Nayak et al., Current Science, 2015, 109, 1445–1457). The STP is hybrid as it uses two different technologies for solar power collection, namely Parabolic Trough Collector (PTC) for heating oil and a Linear Fresnel Reflector (LFR) for generating direct steam. Superheated steam, generated using heat exchangers, subsequently drives the turbine generator block to generate electricity. In the current work, we develop decentralized controllers which ensure safe operation while meeting the production target of the hybrid STP. Towards this end, key control loops in the plant are identified. Continuous transfer function models are identified for these control loops using step tests. PID controllers are then obtained for these loops based on the resulting transfer function models. Wherever relevant, the feedback action of PID controllers is supplemented by a feedforward control action that reacts to the disturbances. Override control action is also implemented to ensure safe operation. The utility of the proposed plantwide decentralized control scheme is demonstrated via simulation studies by comparing the performance of the hybrid STP under open-loop and closed-loop in presence of disturbances and significant dynamic variability in the plant operation via two case studies. Results indicate significantly superior performance of closed-loop operation across various performance metrics.

Evaluation of daily Suspended sediment load using Deep learning models

Abstract: : Precise and reliable suspended sediment load (SSL) prediction models can help ensure the integrated water resources management in a river basin. This study considered two deep learning (DL) models i.e. gated recurrent unit (GRU) and long short term memory (LSTM) for daily SSL modeling in the Missouri River at Omaha, NE gauging station in the United States. The established models are verified with various statistical measures. The assessment of prediction accurateness of the DL models showed that the GRU was the best model in SSL prediction. The coefficient of determination was 0.871 for the GRU and 0.865 for LSTM. Besides, the GRU model has a lesser root mean square error (RMSE) and mean absolute error (MAE) compared to the LSTM. In summary, both the developed DL model can be used competently in SSL modeling. However, GRU utilizes less training constraints and thus uses a lesser amount of memory, performs quicker, and trains faster than LSTM, while LSTM needs additional data to be more accurate.

Ergonomic assessment of male and female workers engaged in makhana processing

Abstract: The present study was planned to assess physiological cost of work, musculoskeletal problem and perceived exertion faced by male and female workers in performance of makhana processing. 20 male and 20 female workers adopted through snowball sampling from Manigachhi block, Darbhanga district of Bihar state. The results of the study indicated that the different activities of both male (90%) and female (100%) in storage of seeds perceived “Very heavy” activity. It was observed that average TCCW of both male and female workers was found to be highest while performing storage of seeds of (3720 beats)) and female (3820 beats). Likewise the average PCW of both male (62.66 beats/min) and female (63.66 beats/min) workers was more during storage of seeds activity. The majority of the respondents both male (80%) and female (85%) were found suffered from “Very Severe” knee pain due to tempering and roasting activities.

Information Theoretic Approach to Reliability Based Sensor Placement for Fault Detection and Diagnosis

Abstract: In sensor placement literature, reliability has been considered to be a point value, corresponding to a specific time. Thus, sensor placement obtained by maximizing reliability changes if time were changed. We propose a cumulative residual Kullback-Leibler divergence based sensor placement formulation for fault detection and diagnosis, which explicitly incorporates time. The formulation also provides an opportunity to the end-user to specify the target performance for sensor placement. Further, we use a greedy algorithm to solve proposed sensor placement design problem. We illustrate the applicability of the approach using a benchmark case study.

Correction to: Continuous–discrete filtering techniques for estimating states of nonlinear differential–algebraic equations (DAEs) systems

Abstract: A Correction to this paper has been published: 10.1007/s40435-022-00955-z

Sensor Fault Accommodation for a Plug Flow Reactor using an M-Estimator

Abstract: The presence of gross errors in the measurements can lead to biased state estimates when conventional Bayesian estimators are used. This can hamper the model-based monitoring and control schemes that rely on the accurate state estimates. In this work, we have developed a framework for robust estimation of the state profiles for Distributed parameter systems (DPSs), in the presence of biased measurements. The proposed approach uses an M-estimator to identify the faulty sensor. The sensor fault diagnosis is then used to augment the state estimator with an extra state that estimates the drifting sensor bias. The proposed approach has been applied to an Auto-Thermal tubular reactor system. The proposed scheme successfully isolates the biased temperature sensors and includes or removes additional bias states as and when required. The gross errors/biases are estimated and subsequently accommodated to provide accurate estimates of spatial profiles of reactor concentration and temperature.