Dr. Iftikhar got MS and PhD degrees in Process Systems Engineering (PSE) from Kyoto University Japan in 2011 and 2014, respectively. PSE incorporates computational and mathematical methods in chemical engineering to solve real industrial problems. His work specifically focuses on gray-box modeling for prediction and control, fault diagnosis systems, uncertainty quantification, and automation of process energy management.
In a project with Kansai Electrical Safety and Inspection Association, Japan, he developed an intelligent fault diagnosis system which can detect causes of breakdown of electric supply lines and help them to quickly restore availability of electricity to customers. In another project with Japan’s largest steelmaking company Nippon Steel and Sumitomo Metal Corporation, Ltd. (NSSMC), he developed an intelligent process control and quality predictor. The work for the steelmaking company earned him the Outstanding Paper of the Year 2014 award in the area of PSE from the Society of Chemical Engineers, Japan.
In a project based on oil refinery process, development of molecular level design of naphtha reforming process, intelligent system for octane number prediction and online energy optimizer lead to an MOU (in process) with the Pakistan’s leading oil refinery, Attock Refinery Limited (ARL). This work earned him the ORIC Innovation Award at the Conference on Sustainability in Process Industries (SPI-16), Peshawar, Pakistan.
Dr. Iftikhar has developed my own research group of graduate students at SCME. Several research projects are being taken by these students. These projects are from a diverse range of subjects such as soft sensors design for prediction of gasoline quality, membrane reactor design, CFD based exergy analysis, cut point temperature optimization, modeling the wastewater treatment process, and modeling the tumor growth.
Sample Research Outcome: For realizing an energy efficient design, the concept of exergy (useable energy) has been getting the attention of researchers and process designers. In one his current project, a stochastic model is developed for prediction of exergy efficiency of naphtha reforming process with uncertainty in the form of probability distributions. Furthermore, a mechanism based on an integration of Artificial Neural Networks (ANN) and Genetic Algorithm is devised to optimize the process parameters when exergy efficiency falls below a set threshold limit. The model/tool is named as RefiENER and is being tailored for online application in ARL, Pakistan.