INTELLIGENT AND SUSTAINABLE PROCESS SYSTEMS LAB (ISPSL)
Home to Pourkargar Research Group @ Kansas State University
WELCOME TO ISPSL
We advance enabling technologies designed to enhance predictive modeling, automation, optimization, and real-time decision-making for complex chemical, biological, energy, and food systems!
The research in our lab focuses on developing intelligent frameworks and the corresponding computational tools needed for
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Controlling complex process networks,
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Applied artificial intelligence in chemical, biological, and energy systems,
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Designing cyber-physical architectures for smart process manufacturing,
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Advancing system identification using machine learning and process data analytics.
Most of our work is computational, but we are intensely interested in laboratory automation to test hypotheses, validate model predictions, and design autonomous experiments.
Our research integrates process systems engineering, artificial intelligence, computational multiscale modeling, and digital twin technology to address challenging problems in chemical, material, food, and bioengineering!
CONTACT US
Office:
2017 Durland Hall, 1701A Platt St.
Manhattan, KS 66506
Computational Lab:
2056 Durland Hall
Experimental Labs:
2002, 2043 Durland Hall
Tel: 785-532-5584
Email: dbpourkargar (at) ksu (dot) edu
FOLLOW OUR RESEARCH
NEWS & UPCOMING EVENTS
October 2024
We have given six talks and a poster presentation at the 2024 AIChE Annual Meeting in San Diego, CA:
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Keynote 1 - Resilient Multi-Agent Estimation and Control of Complex Process Networks (702a @ Cybersecurity and High-Performance Computing in Next-Gen Manufacturing Session) Link
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Predictive Modeling and Optimal Regulation of Modular Hydrogen-Ammonia Systems with Renewable Energy Integration for Resilient Chemical-Energy Conversion (564d @ Next-Gen Manufacturing in Chemical and Energy Systems Session) Link
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A Physics-Informed Deep Learning Approach to Predict Soil Water Content for Agricultural Decision-Making (579a @ Data-Driven and Hybrid Modeling for Decision-Making Session) Link
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Predictive Modeling and Optimal Control of a Particulate Polysilicon Reactor System for Enhanced Solar Cell Manufacturing (617c @ Modeling, Optimization, and Control in Next-Gen Manufacturing Session) Link
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Dynamic Graph-Based Distributed Estimation and Control of Fast-Evolving Complex Process Networks (664f @ Applied Artificial Intelligence, Big Data, and Data Analytics Methods for Next-Gen Manufacturing Efficiency Session) Link
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Time-Series Integrated Surrogate Modeling for Control of Ammonia Synthesis and Adsorption Processes (711a @ AI/ML Modeling, Optimization, and Control Applications II Session) Link
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A Distributed Data-Driven Predictive Modeling Approach for Cyber-Process Incident Identification Using Spectral Community Detection (375d @ Interactive Session: Data and Information Systems) Link