Browsing by Author "Shrivastava, R."

Now showing 1 - 5 of 5

Application and Evaluation of Random Forest Classifier Technique for Fault Detection in Bioreactor Operation
(2017) Shrivastava, R.; Mahalingam, H.; Dutta, N.N.
Bioreactors and associated bioprocesses are quite complex and nonlinear in nature. A small change in initial condition can greatly alter the output product quality. It is pretty difficult at times to model the system mathematically. In this work, the fault detection problem is studied in the context of bioreactors, mainly, a reactor from the penicillin production process. It is very important to identify the faults in a live process to avoid product quality deterioration. We have focused on the process history-based methods to identify the faults in a bioreactor. We want to introduce random forest (RF), a powerful machine learning algorithm, to identify several types of faults in a bioreactor. The algorithm is simple, easy to use, shows very good generalization ability without compromising much on the classification accuracies, and also has an ability to give variable importance as a part of the algorithm output. We compared its performance with two popular methods, namely support vector machines (SVM) and artificial neural networks (ANN), and found that the overall performance is superior in terms of classification accuracies and generalization ability. 2017, Copyright Taylor & Francis Group, LLC.
Application and Evaluation of Random Forest Classifier Technique for Fault Detection in Bioreactor Operation
(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2017) Shrivastava, R.; Mahalingam, H.; Dutta, N.N.
Bioreactors and associated bioprocesses are quite complex and nonlinear in nature. A small change in initial condition can greatly alter the output product quality. It is pretty difficult at times to model the system mathematically. In this work, the fault detection problem is studied in the context of bioreactors, mainly, a reactor from the penicillin production process. It is very important to identify the faults in a live process to avoid product quality deterioration. We have focused on the process history-based methods to identify the faults in a bioreactor. We want to introduce random forest (RF), a powerful machine learning algorithm, to identify several types of faults in a bioreactor. The algorithm is simple, easy to use, shows very good generalization ability without compromising much on the classification accuracies, and also has an ability to give variable importance as a part of the algorithm output. We compared its performance with two popular methods, namely support vector machines (SVM) and artificial neural networks (ANN), and found that the overall performance is superior in terms of classification accuracies and generalization ability. © 2017, Copyright © Taylor & Francis Group, LLC.
Graph representational learning for bandgap prediction in varied perovskite crystals
(Elsevier B.V., 2021) Omprakash, P.; Manikandan, B.; Sandeep, A.; Shrivastava, R.; Viswesh, P.; Bhat Panemangalore, D.B.
Perovskites are an important class of materials that are actively researched for applications in solar cells and other optoelectronic devices due to their ease of fabrication and tuneable bandgaps. High throughput computational techniques like Density Functional Theory (DFT) and Machine Learning (ML) are viable methods to accelerate discovery of new perovskite materials with favourable properties. ML specifically is faster and requires lesser computational power. We recognized the importance of having robust datasets for ML and hence collated a dataset of varied perovskite structures along with their indirect bandgaps. We employed a graph representational learning technique and trained a model that predicted bandgaps for all types of perovskites. The model has a mean absolute error of 0.28 eV and can predict bandgap in a few milliseconds. The metric of generalization gap is introduced to quantify the performance of ML models. This metric will help in building more generalized models that can predict properties for novel materials. Furthermore, we believe that these computational techniques should be user-friendly to those less experienced in the field. Hence, for researchers unacquainted with DFT or ML, we built a pipeline that abstracts the specific processes. This makes it easier for material scientists to quickly screen viable inorganic perovskite compounds allowing them to synthesize and experiment on the more promising compounds. © 2021 Elsevier B.V.
Isolation and characterization of phosphorus solubilizing bacteria from manganese mining area of Balaghat and Chhindwara
(2017) Dixit, S.; Kuttan, K.K.A.; Shrivastava, R.
Plants require optimum amount of available phosphorus to support their growth and development. Phosphorus is known to have significant role in root subdivision, vitality and disease resistance of plants. Different types of bacteria involved in phosphorus solubilization can be used as biofertilizer in reclamation of mining area. The present study deals with isolation and identification of phosphorus solubilizing bacteria from the manganese mining area of Balaghat and Chhindwara districts of Madhya Pradesh, India. rDNA (16s) based molecular identification was performed assisted by MEGA phylogenetic analysis. Pseudomonas putida, Bacillus licheniformis, Pseudomonas taiwanensis and Pseudomonas aeruginosa were explored as potential phosphorus solubilizers from the selected sites.
Isolation and characterization of phosphorus solubilizing bacteria from manganese mining area of Balaghat and Chhindwara
(Indian Academy of Sciences, 2017) Dixit, S.; Kuttan, K.K.A.; Shrivastava, R.
Plants require optimum amount of available phosphorus to support their growth and development. Phosphorus is known to have significant role in root subdivision, vitality and disease resistance of plants. Different types of bacteria involved in phosphorus solubilization can be used as biofertilizer in reclamation of mining area. The present study deals with isolation and identification of phosphorus solubilizing bacteria from the manganese mining area of Balaghat and Chhindwara districts of Madhya Pradesh, India. rDNA (16s) based molecular identification was performed assisted by MEGA phylogenetic analysis. Pseudomonas putida, Bacillus licheniformis, Pseudomonas taiwanensis and Pseudomonas aeruginosa were explored as potential phosphorus solubilizers from the selected sites.