Prof. Syafiie Syamaun is a Professor of Chemical and Materials Engineering at King AbdulAziz University (KAU), Jeddah, Saudi Arabia, with extensive academic and research experience in control theory, systems engineering, process dynamics, and intelligent control applications.
He earned his Ph.D. in Systems Engineering and Automatic Control from the University of Valladolid, Spain, with a focus on model-free learning control for chemical processes. His career includes postdoctoral research in Spain and Belgium, a decade as Senior Lecturer at Universiti Putra Malaysia, and progressive academic leadership at KAU, where he has served as Head of Thermal and Analytical Laboratories.
Prof. Syafiie's research bridges control theory development, time-delay systems, process engineering, energy systems, robotics, and healthcare applications. He has pioneered advanced techniques in reinforcement learning control, H∞ Control; controller synthesis, and model predictive control, with impactful applications in diabetes treatment, anesthesia regulation, and chemical processes.
Beyond academia, Prof. Syafiie has led internationally funded research projects in areas such as bionic pancreas systems, energy optimization, and accident risk analysis, while also consulting on industrial process safety and control system design. He has supervised numerous Ph.D. and Master’s theses, contributing to talent development across Malaysia, Indonesia, and Saudi Arabia. He also founded Data intelligence analytics and Fiberterra Network Indonesia companies.
A frequent invited lecturer and keynote speaker, he has delivered talks on artificial intelligence, control systems, and Industry 4.0 across universities and conferences in Asia and the Middle East. He is an active member of multiple professional societies including IEEE, ISA, IChemE, and the European Mathematical Society, and serves as a reviewer for leading journals.
Prof. Syafiie's current work emphasizes data-driven modeling, time-delay systems, and intelligent control solutions for real-world challenges in engineering, healthcare, and sustainability.
Key words: Process Control, Biomedical systems, Machine Learning, Predictive Control, Time Delay Systems, Stability and H∞ Control.
Research Interests
My research lies at the intersection of systems and control theory, data-driven dynamic
modeling, and process engineering, addressing applications across chemical
engineering, energy systems, robotics, and healthcare. A central
theme of my work is the development of robust controllers, observer synthesis,
and learning-based frameworks for deterministic and stochastic systems with timevarying
delays, bridging mathematical rigor with real-world engineering challenges.
Machine Learning
Agentic AI
Syafiie’s central research contributions lie at the intersection of control engineering,
reinforcement learning, and chemical process systems. His work tackles the
critical problem that many real-world chemical processes—such as pH control, oxidation reactions, and wastewater
treatment—are highly nonlinear, time-varying, and difficult to model accurately using first-principles approaches.
To address this, Syafiie has extensively developed and validated a framework known as
Model-Free Learning Control (MFLC), which adapts reinforcement learning (RL)—
particularly Q-learning—to practical, real-time process control applications.
Within learning control or adaptive control frameworks, one seeks to adjust controller parameters in real time
(or across repeated trials) to track desired trajectories or reject disturbances. His focus on learning control
suggests that he works with techniques such as iterative learning control (ILC),
reinforcement learning, adaptive model reference control, or other data-driven regulators.
Such control frameworks allow maintaining performance even in the absence of full system models.
MFLC provides a natural foundation for integration with Agentic AI,
combining adaptive control theory with autonomous artificial intelligence to solve complex real-world problems.
MFLC eliminates the need for explicit mathematical models by learning directly from input-output data,
adjusting control actions iteratively to maintain performance under uncertainty.
Agentic AI, on the other hand, represents a higher-level framework where intelligent agents perceive their environment,
plan, reason, and act autonomously toward defined goals.
When integrated, MFLC serves as the adaptive control core, ensuring real-time stability and robustness,
while Agentic AI functions as the cognitive layer that interprets context, sets goals,
and optimizes performance across broader operational scenarios.
Control Theory
Time delay systems
Dr. S. Syafiie’s research in control theory for time-delay systems
focuses on the development of robust and adaptive control strategies that account
for delays occurring in practical engineering and biological processes.
Time delays are common in chemical reactors, networked control systems, and physiological
regulation—where transport, communication, or actuation lags can destabilize feedback loops.
Dr. Syafiie has explored H-infinity control and state-observer design for systems
with multiple time delays, as evidenced by his invited lectures such as
“H-infinity State-Observer Controller Synthesis for Multiple Time-Delay Systems”
(King Abdulaziz University, 2019) and
“Positive Evolution Control of Diabetes Delays Systems”
(UPM/IEEE CSS Malaysia, 2016). His work highlights how delay-dependent stability analysis,
Lyapunov-Krasovskii methods, and linear matrix inequalities (LMIs) can be
combined to ensure robust control and estimation even when system responses are delayed or uncertain.
Building on his broader expertise in model-free and learning-based control,
Dr. Syafiie applies these frameworks to manage dynamic processes with significant lags or
unknown delay parameters. His designs often integrate robust H-infinity synthesis
with adaptive or predictive mechanisms that compensate for delays in real time, maintaining stability
and performance under changing conditions. Applications of his methods include chemical process control,
where reaction and transport delays are unavoidable, and biomedical systems such as glucose-insulin regulation,
where physiological delays play a key role. Through these contributions,
Dr. Syafiie advances the control theory of time-delay systems by linking rigorous mathematical design
with practical engineering and biomedical implementations,
ensuring reliability and robustness in systems inherently affected by delay phenomena.
Applications
Real World Problems
Dr. Syafiie has applied his expertise in control theory, especially model-free learning control,
robust control (H∞), PID/LQG controllers, and observer/controller synthesis, to a number of engineering,
biomedical, and industrial contexts. One recurring application area is biomedical systems, particularly diabetes
nd drug/insulin delivery. For example, he has published articles and delivered lectures on Automated Glycemic Control
with a Bionic Pancreas, showing how control methods (PID, LQG, H∞) can be used to manage physiological dynamics
and deliver insulin in a closed-loop system. He also addresses control in anesthesia and drug regulation,
demonstrating the translation of control-theoretic solutions to patient safety and clinical contexts.
Another major application domain is chemical and process engineering: process control in mixing processes,
pH neutralization, feedback control in oil industry (oil wells), and refinery engineering.
He also lectures on control design for processes in the context of Industry 4.0, where automation, digitalization,
and smart control become critical. He works on applying advanced control methods to industrial process units, chemical plants, and
infrastructures that require stability, performance under uncertainty, and efficient control.
My Publications
Noerhidajat Sjahro, R. Yunus, Marihot Nainggolan, A. Arifin, S. Syafiie, Zurina A. A., Umer Rashid, Vicknes R. Thang Yin Mee,
Effect of Different Pressures of Sterilization and Duration of Digestion on Oil Extraction in Palm Oil Milling Process, submited
Boon Chong Choo,Musab Abdul Razak, Mohd Zahirasri Mhd Tohir, Dayang Radiah Awang Biak, S. Syafiie,
AI-Driven Accident Prediction and Risk Assessment for Hydrogen Safety, submited
Sisilia Sylviani, S. Syafiie, Intan Nurma Yulita, Performance and Sustainability Challenges in Networked Control Systems: A systematic literature
review, submited
A. Subiantoro, S. Jones, N. Nasruddin, S. Syafiie, Comfortable Achievement in Complex, Multi-zone Environments of A HVAC-VRF System by
Multi-objective Function of Datadriven Neural Network Model Predictive Control, submited
Chang, J.J., Syafiie, S., Ahmad, R.K.R. and Lim, T.A.,
ANFIS based model for Bispectral Index prediction.In Recent Advances on Soft Computing and Data Mining: Proceedings of The First International Conference on Soft Computing and Data Mining (SCDM-2014)
Universiti Tun Hussein Onn Malaysia, Johor, MalaysiaJune 16th-18th, 2014 May 29(pp. 133-142). Cham: Springer International Publishing.
Valizadeh M, Syafiie S, Ahamad IS.
Multi-objective particle swarm optimization for optimal planning of biodiesel supply chain in Malaysia.In Recent Advances on Soft Computing and Data Mining: Proceedings of The First International Conference on Soft Computing and Data Mining (SCDM-2014)
Universiti Tun Hussein Onn Malaysia, Johor, MalaysiaJune 16th-18th, 2014 May 29 (pp. 293-302). Cham: Springer International Publishing.
Syafiie S, Tadeo F, Martinez E.
Q (λ) learning technique for pH control.In 2009 IEEE International Conference on Industrial Engineering and Engineering Management 2009 Dec 8 (pp. 712-716).
Syafiie S, Niño J, Ionescu C, De Keyser R.
NMPC for propofol drug dosing during anesthesia induction.In L. Magni et al. (Eds.): Nonlinear Model Predictive Control: towards new challenging applications 2009 May 18 (pp. 501-509).
Berlin, Heidelberg: Springer Berlin Heidelberg.
Syafiie S, Tadeo F, Martínez EC.
Look up table for level control of two tanks system.In 6th International Workshop on Practical Applications on Agents and Multiagent Systems 2007 (pp. 191-200).
Universidad de Salamanca.
S. Syafiie, C. Fernandez, M. Garcia, A. Alonso and F. Tadeo,
Reinforcement Learning-Based Control for Batch Thermal Sterilization of Canned Foods,
III Simposio de Control Inteligente, Mallorca 6-8 June 2007.
Syafiie, S., Tadeo, F. and Martinez, E.,
Model-Free Learning Control for processes with constrained incremental control.In 2006 IEEE Conference on Computer Aided Control System Design,
2006 IEEE International Conference on Control Applications,
2006 IEEE International Symposium on Intelligent Control Munich, Germany, October 2006, pp. 826-831.
Syafiie S, Tadeo F, Martínez EC.
Agents coordination to control oxidation process. In Fifth International Workshop on Practical Applications of Agents and Multiagent Systems (IWPAAMS'06): October 19-20, 2006, EU Informática-Segovia (Spain), U. of Valladolid: proceedings 2006 (pp. 109-123). Universidad de Valladolid.
S. Syafiie, F. Tadeo and E. Martinez,
Model-Free Intelligent Control of Neutralization Processes: Experiments on a Laboratory Plant,
I Simposio Sobre Control Intelligente, 1st-3rd June 2005, Huelva, Spain
Syafiie S, Tadeo F, Martinez E.
Learning control applied to ph plant.In 7th International Symposium on Dynamics and Control of Process System (DYCOPS) July 2004, Boston, USA.
Syafiie Syam and Muhammad Sobron,
Automation: From Kitchen to Aircraft, Waspada Daily Newspaper (23 September 2003, in Bahasa Indonesia)
Annur Suhadi, Syafiie, and Marwan,
Stiff Problem Solution using Adams Method in Simulation of Packed Reactive Distillation Column,
Proceeding of International Seminar in Numerical Analysis in Engineering, 2000, Medan, Indonesia
Syafiie, Annur Suhadi, Syaubari, and Marwan,
Numerical Solution of Electrokinetics Mass Transfer Model for Hemoglobin Recovery in an Ionic PolyCarbonate Membrane,
Proceeding of International Seminar in Numerical Analysis in Engineering, 2000, Medan, Indonesia
Subject Teaching
Process Control
A process control class covers the fundamental principles and practices of automatically regulating
chemical industrial processes, focusing on maintaining variables like temperature, pressure, and flow at a set point.
Key topics include dynamic modeling (using mathematical tools like Laplace transforms),
designing and tuning controllers (especially PID control), analyzing process dynamics,
and understanding instrumentation like sensors and actuators (including control valves).
The goal is to ensure process safety, product quality, and efficiency, often using feedback control loops
to make real-time adjustments.
Numerical methods provide approximate solutions to complex mathematical problems in chemical engineering,
such as those encountered in process simulation, reaction kinetics, and transport phenomena.
Key applications include solving differential equations for reactor and heat exchanger design using well known methods
like the Finite Difference Method and Finite Element Method (FEM),
finding roots of linear and nonlinear system equations for equilibrium calculations with the Newton-Raphson method and
Descent Gradient method, and using integration techniques to solve ordinary differential equations for process dynamics
by using Runge-Kutta method. These methods are indispensable for process optimization and data analysis when analytical solutions are
impractical or impossible.
Thermodynamics for chemical engineering is the study of energy, work, and their relation to matter,
especially in the context of chemical reactions and physical changes. It is used to determine the
feasibility and direction of processes, perform energy balances, and design equipment like reactors
and separations units by applying the fundamental laws of thermodynamics, particularly the first and second laws.
Key applications include analyzing phase and chemical equilibria, optimizing process efficiency, and designing
systems for heating, cooling, and power generation.
Separation processes in chemical engineering are techniques used to divide mixtures into purer components
by exploiting differences in their physical or chemical properties, such as boiling point, density, or solubility.
Common methods include distillation (based on boiling point), filtration (separating solids from liquids),
membrane separation (using semipermeable membranes), extraction (using a solvent), and crystallization
(forming a solid from a solution). These processes are essential for purifying products, recovering valuable
materials, and managing waste in many industries.
Amirreza Moghada Haghani, Fuzzy Method Based Analytical Study and Numerical Algorithm to Computer Reliability and Availability of Steam Cogeneration Unit
Nurhidayat, Modeling and Simulation of Oil Palm Fruit Digestion Process
Ramin Khezri, Fuzzy Control of CSTR in Biodiesel Production
Hamid Pourzolfaghar, H2S Adsorption from Biogas using Natural Clinoptilolite
Cosmin Copot, Automatic Carpet Wear Classification based on Support Vector Machine
Invited Lecturer and Keynote Speaker
Dr. Syafiie has been invited for many lecturer and keynote speakers
Opportunities, Prospect and Challenges for Chemical Engineers toward Revolution Industry 4.0,
Lokakarya, Faculty of Engineering, Malikussaleh University, 21 June 2019.
Art and Design Closed Loop Systems for Anesthesia Drug Regulation to Patient in Operating Theater,
Kulliyyah of Engineering, International Islamic University Malaysia, Malaysia. 4 October 2013.
Control in Biomedical Engineering, Institute of Engineers Malaysia, Petaling Jaya, Malaysia. 12 July 2010.