Scheme for Promotion of Academic and Research Collaboration (SPARC)

Indian PI(s): Dr. Kishalay Mitra / Dr. Raja Banerjee

University of Exeter PI(s): Dr. Richard Everson / Dr. Jonathan Fieldsend

Scholars(s): Dr. Srinivas Soumitri, Dr. Prateek Mittal, Ms. Keerthi NagaSree Pujari, Dr. Pantula Devi Priyanka, Dr. Inapakurthi Ravi Kiran 

Summary

Wind energy is now the second fastest growing source of electricity in the world. To harness wind energy, turbines are placed in certain locations and together they constitute a wind farm. Due to nonlinear objectives and constraint functions with integral (number of turbines) and real (location of turbines) decision variables, wind farm layout optimization becomes a multi-objective mixed integer nonlinear programming (MINLP) problem which is hard to solve. The proposed work aims at enabling users to handle variable wind speed condition, as opposed to persisting assumption of constant wind speed, using machine learning for the evolution of probability density of wind speed occurrence and then solving the problem of wind farm layout optimization, under uncertainty using robust Bayesian Optimization techniques. The novelty in this work is: (i) Auto-tuning of LSTM based machine learning networks using a two-objective evolutionary algorithm. (ii) Proposed two-objective Integer Nonlinear Programming (INLP) framework led to the automated development of parsimonious LSTM networks with maximum prediction accuracy. (iii) Optimal estimation of activation functions in LSTMs. (iv) Application of optimal LSTMs for modelling real wind speed and direction data collected from wind farm in France. (v) Long term forecasting (up to 2 years) of wind data using optimal LSTM networks. (vi) Robust layout optimization under wind state uncertainty.

Publications

Journals

• Mittal, P., Mitra, K., In Search of Flexible and Robust Wind Farm Layouts Considering Wind State Uncertainty, Journal of Cleaner Production, 2020, 119195.

• Mittal, P., Mitra, K., Micro-siting under practical constraints addressing the energy-noise-cost trade-off, Wind Energy, 2020, 23, 1905-1918.

• Pantula, P. D., Mitra, K., Towards Efficient Robust Optimization using Data based Optimal Segmentation of Uncertain Space, Reliability Engineering and System Safety, 197, 2020, 106821.

• Pantula, P. D., Soumitri M. S., Mitra, K., An Evolutionary Neuro-Fuzzy C-means Clustering Technique, Engineering Applications of Artificial Intelligence, 89, 2020, 103435. 

• Soumitri M. S., Mitra, K., Deep Learning based System Identification of Industrial Integrated Grinding Circuits, Powder Technology, 360, 2020, 921-936.

• Pantula, P. D., Mitra, K., A Data-Driven Approach Towards Finding Closer Estimates of Optimal Solutions Under Uncertainty for an Energy Efficient Steel Casting Process, Energy, 189, 2019, 116253.

• Ravi Kiran, I., Pantula, P. D., Soumitri M. S., Mitra, K., Data driven robust optimization of grinding process under uncertainty, Materials and Manufacturing Processes, 2020, 35, 1870-1876.

• Ravi Kiran, I., Soumitri M. S., Mitra, K., Recurrent Neural Networks based Modelling of Industrial Grinding Operation, Chemical Engineering Science, 219, 2020, 115585.

• Virivinti, N., Hazra, B., Mitra, K., Optimizing Grinding Operation with Correlated Uncertain Parameters, Materials and Manufacturing Processes, 2021, 36(6), 713-721.

• Ravi Kiran, I., Soumitri M. S., Mitra, K., Deep Learning Based Dynamic Behaviour Modelling and Prediction of Particulate Matter in Air, Chemical Engineering Journal, 2021, 426, 131221.

• Gumte, K., Pantula, P. D., Soumitri M. S., Mitra, K., Data Driven Robust Optimization for Handling Uncertainty in Supply Chain Planning Models, Chemical Engineering Science, 2021, 246, 116889.

• Gumte, K., Pantula, P. D., Soumitri M. S., Mitra, K., Achieving Wealth from Bio-Waste in a Nationwide Supply Chain Setup under Uncertain Environment through Data Driven Robust Optimization Approach, Journal of Cleaner Production, 2021, 291, 125702.

• Sharma, S., Pantula, P. D., Soumitri M. S., Mitra, K., A Novel Data-driven Sampling Strategy for Optimizing Industrial Grinding Operation under Uncertainty using Chance Constrained Programming, Powder Technology, 2021, 377, 913-923.

• Ravi Kiran, I., Naik, S., Mitra, K., Towards Faster Operational Optimization of Cascaded MSMPR Crystallizers using Multi-objective Support Vector Regression, Ind. Eng. Chem. Res. 2022, 61, 11518−11533.

• Krishnan, K. J., Mitra, K., A Modified Kohonen Map Algorithm for Clustering Time Series Data, Expert Systems With Applications, 2022, 201, 117249.

• Soumitri M. S., Pujari, K. N., Naik, S., Mitra, K., Evolutionary Neural Architecture Search for Surrogate models to Enable Optimization of Industrial Continuous Crystallization Process, Powder Technology, 2022, 405, 117527.

• Ravi Kiran, I., Mitra, K., Optimal Surrogate Building Using SVR for an Industrial Grinding Process, Materials and Manufacturing Processes, 2022, 37, 1701-1707.

Conferences

• Ravi kiran I., Mitra, K., System Identification and Process Modelling of Dynamic Systems Using Machine Learning, 26th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, October, 2022.

• Ravi kiran I., Mitra, K., Artificial Intelligence Assisted Optimization Under Uncertainty for Robust Solutions, 26th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, October, 2022.

• Ravi kiran I., Mitra, K., Data Based Time Series Modelling of Industrial Grinding Circuits, International Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2022), Goa, INDIA, September 2022.

• Ravi kiran I., Mitra, K., Machine Learning Based Surrogate Assisted Multi-Objective Optimization of Continuous Casting Process, Seventh IEEE Indian Control Conference, Bombay, India, Dec 2021.

• Krishnan, K. J., Mitra, K. Clustering Time Series Sensor Data Using Modified Kohonen Maps, Seventh IEEE Indian Control Conference, Bombay, India, Dec 2021.

• Pantula, D. P., Miriyala, S. S., Mitra, K., A Deep Unsupervised Learning Algorithm for Dynamic Data Clustering, Seventh IEEE Indian Control Conference, Bombay, India, Dec 2021.

• Pujari, K., Srivastava, V., Miriyala, S. S., Mitra, K., Comparison of Deep Reinforcement Learning Techniques with Gradient Based Approach in Cooperative Control of Wind Farm , Seventh IEEE Indian Control Conference, Bombay, India, Dec 2021.

• Ramamurthy, A., Pantula, P.D., Gharote, M., Mitra, K., Lodha, S., Multi-Objective Optimization for Virtual Machine Allocation in Computational Scientific Workflow under Uncertainty, 11th International Conference on Cloud Computing and Services Science (CLOSER), April 2021, 240-247. 

• Miriyala, S. S., Chowdhury, S., Keerthi, N. P. and Mitra, K., Optimally designed Variational Autoencoders for Efficient Wind Characteristics Modelling, 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI), Canberra, Australia, December 1 - 4, 2020.

• Miriyala, S. S., Banerjee, R. and Mitra, K., Uncertainty quantification using Auto-tuned Surrogates of CFD model Simulating Supersonic flow over tactical missile body, 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI), Canberra, Australia, December 1 - 4, 2020.

• Keerthi, N. P., Miriyala, S. S. and Mitra, K., Auto-tuned Deep Recurrent Neural Networks for Application in Wind Energy Conversion Systems, 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI), Canberra, Australia, December 1 - 4, 2020.

• Keerthi N. P., Soumitri M. S., Mittal, P., Mitra, K., Optimal Long Short Term Memory Networks for long-term forecasting of real wind characteristics, 2020 Advances in Control & Optimization of Dynamical Systems, IIT Chennai.

• Ravikiran, I., Soumitri, M. S., Mitra, K., Modelling of pollutants and particulate matter in air using auto-tuned deep recurrent networks, 2020 Advances in Control & Optimization of Dynamical Systems, IIT Chennai.

• Soumitri, M. S., Nagalla, S. H., Mitra, K., Comparative study of optimal Long Short Term Memory Networks (LSTMs) for one day ahead solar irradiance hourly forecast, Sixth IEEE Indian Control Conference 2019.

• Mittal, P., Mitra, K., Robust Wind Farm Layout Optimization under Uncertainty, Sixth IEEE Indian Control Conference 2019.

• Pantula, P. D., Mitra, K., An Evolutionary Machine Learning Approach Towards Less Conservative Robust Optimization, IEEE Congress on Evolutionary Computation, Wellington, New Zealand, June 10-13, 2019, 2990-2997.

• Mittal, P., Mitra, K., Variable Grid Resolution based Evolutionary Multi-objective optimization towards Micro-siting, IEEE Congress on Evolutionary Computation, Wellington, New Zealand, June 10-13, 2019, 2787-2793.

Book

• "Optimization, Uncertainty & Machine Learning in Wind Energy Conversion Systems” edited by Prof(s) K. Mitra, R. Everson and J. Fieldsend, Springer under the book series, “Engineering Optimization” (in press).