AI Based Cleaning Scheduler System for Heat Exchangers

Fouling in heat exchanger preheat trains is a well-known problem in the refining & chemical industries and has a direct impact on environment & plant profitability and available capacity.?

To combat fouling, heat exchangers must be periodically cleaned to improve heat transfer coefficient. However, the maintenance of Heat Exchangers in PHTs has four major challenges:?

• Impact of Fouling: Heat exchanger fouling causes an increase in furnace firing & GHG Emissions, leading to increased operating costs.??
• Furnace firing increases because of Net heat loss due to exchanger taking-out / furnace efficiency?
• Network Effect:?Taking out a HeatEx for maintenance impacts the efficiency and performance of the downstream exchangers in the network??
• Economic Trade-Off: Frequent cleaning of exchangers increases maintenance expenditures & infrequent cleaning has the potential to increase fouling, leading to higher energy consumption?

The Indian Institute of Technology (IIT) Jodhpur has collaborated with Algo8 AI Private Limited to develop a data pipeline build-up and machine learning model for heat exchangers of oil and petroleum refineries. Professor Pradip Kumar Tewari, Head, Department of Chemical Engineering, and Dr Angan Sengupta, Assistant Professor, Department of Chemical Engineering and Affiliated Faculty in SAIDE, IIT Jodhpur, along with their research team of postgraduate students are providing consultation service for this project.

Speaking about the problem statement of this project, Professor Pradip Kumar Tewari, Head, Department of Chemical Engineering, IIT Jodhpur, said, “Researchers at IIT Jodhpur are well capable of dealing with such a fundamental problem with its traditional chemical engineering knowledge base amalgamated with modern modelling and computation-based know-how in the field of chemical engineering.”

Highlighting the project, Dr Angan Sengupta, Assistant Professor, Department of Chemical Engineering and Affiliated Faculty in SAIDE, IIT Jodhpur, said, “This particular project depicts that the present-day chemical engineering, of which computation for establishing industry 4.0 is an integral part, has traversed a long way from traditional chemical engineering and is capable to meet the new challenges of various process industries. To this end, the Department of Chemical Engineering at IIT Jodhpur is well equipped.”

To address the same, the research team has developed a data-based model for control and schedule maintenance of heat exchanger networks to increase the efficiency of refineries. This developed model can address the problem of fouling of heat exchangers in oil refineries and can be applied in any petroleum and allied industries.

Along with this, other objectives of this project include condition monitoring of heat exchanger trains, assessing the network impact of each heat exchanger unit, suggesting the next scheduled date for cleaning, a graphical user interface to provide to visualize the predicted results and verify the accuracy of the model, model-based scheduled maintenance for heat exchanger units at oil refinery sites to reduce overall maintenance costs, model-based improved asset availability with in-time warnings and model-based improved shutdown turn-around time.

Reference: https://www.ndtv.com/education/iit-jodhpur-algo8-ai-collaborate-make-oil-refining-in-petroleum-allied-industries-more-efficient

#artificialintelligence ?#machinelearning ?

#ai ?#predictivemaintenance ?#heatexchangers ?#avu?#refineryoperations ?#oilandgas #data ?#energy ?#software ?

K.K. JAIN?Prabhakar Srivastava?Nishish Jha?Praveen Kumar (Tyagi)? Ashok Ranade? Himanshu Ujjawal Singh? Nandan Mishra? Shalu Agrawal

About Algo8

Algo8 is an Industrial Artificial Intelligence company which is augmenting sustainable growth for process and discrete manufacturing industries. Our AI + First Principles approach has unlocked tremendous business value for over 30 customers, including 6 Fortune 500 companies.