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Company Overview | Infotrol AI Platform

Infotrol technology

Company Overview

Company History

2009: Selected in Small & Medium Business New Technology Development Project in Korea
2008: Company Affiliated R&D institutes foundation and certification
2007: INNO-BIZ certification from Korean Government
2006: Venture Company Certification in Korea
May, 2004: Infotrol Technology Co. Ltd. Founded

Business Area

Advanced Control, Operation Automation and Energy Optimization

Continuous Process APC (Advanced Process Control)
Energy Network Balance, Simulation, Optimization and Planning
Process Automation (Integrated Process Operation Expert System)
Batch and Semi-batch Process APC

Core Technology

Automation, APC and Optimization solution
Continuous and batch process control
Process sequence automation coordinated with advanced control system
Energy Network Optimization and Planning System
Energy network optimization and production planning system
Product Customization
Customization by user’s special requirements
Constant maintenance by fully experienced engineer
State of art in-house development solution

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Leadership & Expertise

CEO : Kim, Weonho

Louisiana State Univ. LA, USA, ChemE PhD
Seoul National University, Seoul Korea, ChemE MS
Hanyang University, Seoul Korea, ChemE BS
Setpoint Inc. MPC Development, Houston, TX, USA
Samsung SDS Chief Consultant, Korea
Honeywell Hi-spec Solution, Singapore Office Korea Manager
AspenTech Korea Business Manager, Korea

Team Structure & Qualifications

Project Career

Development of APC, Automation, and Energy related products
(HPCB, INFOTROL-MPC, ISA/IPOES,ENetOPT, ENetPlan)
Samsung Fine chemical, Samsung Petrochemical, GS Caltex APC and Optimization project
SK, LG Petrochemical, Inchon Petrochem APC and Optimization project
Philippines Petron Oil Refinery APC and Optimization project
Malaysia Petronas, US Texaco Eldorado, Venezuela MaravenOil Refinery APC and Optimization
Qatar NODCO APC lead engineer

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Integrated Solution Architecture

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Infotrol AI Platform

Why AI Now? - Three Key Industry Challenges

Workforce shotage - Aging population, retirement of experienced operators
Increasing complexity - Need for consistent, precise operation in advanced processes
ESG & energy regulations - Demand for energy-efficient and sustainable operations

AI Strategy

AI as an add-on to existing stable control systems
Practical and safe deployment in real plant environments
Combining 3rd Gen Automation (e.g., IPOES, HIECON) with 4th Gen AI (CNN, LSTM, RL)

AI Capabilities

Vision AI (CNN) – Visual recognition and automation
Predictive AI (LSTM) – Accurate forecasting using time-series data
Control AI (RL) – Real-time control optimization via reinforcement learning

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Solutions

Streamline Complex Operations

Design and execute SOPs through a low-code, flowchart-based interface
Automate sequences to reduce human error and manual intervention
Ensure repeatability and compliance across operators and shifts

Integrated Platform for Control & Data

Seamlessly interface with DCS, RTDB, and MES systems
Monitor abnormal conditions and improve operational safety
Accumulate operational know-how and digitize procedures as data

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Model-Based Predictive Control

Apply MPC algorithms to both continuous and batch processes
Improve product quality and process stability through real-time predictions
Reduce variability and optimize process performance

Hybrid & Hierarchical Solutions

Support multi-layered control for batch/semi-batch applications
Enhance flexibility and adaptability with parameter tuning
Enable closed-loop optimization through integration with other systems

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Standardize and Centralize Recipes

Manage all recipe logic, parameters, and versions in one platform
Enable structured recipe creation and modification
Facilitate clear linkage between recipes and equipment logic

Ensure Quality and Compliance

Provide real-time recipe validation and change tracking
Ensure consistent product outcomes across production lines
Integrate easily with IPOES for automated execution

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Optimize Throughput and Utilization

Generate feasible production schedules with demand and capacity constraints
Maximize equipment utilization and minimize changeovers
Balance workload across production units

Real-Time Adjustment and Flexibility

Adapt plans to unexpected changes in demand or equipment status
Evaluate multiple what-if scenarios with simulation tools
Align production plans with supply chain and energy goals

Optimize Utility Cost and Flow

Use MILP-based optimization for energy distribution
Minimize utility costs while meeting operational constraints
Recommend real-time operating strategies by time zone

Close the Loop with Live Data

Apply closed-loop control based on real-time feedback
Reflect forecasted demand and operational changes automatically
Visualize and track optimization KPIs in a central dashboard

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Strategic Planning for Energy Use

Generate long-term energy operation plans by equipment and time
Compare planned vs. actual performance for improvement
Build a predictive and proactive energy management structure

Integrated Forecast and Optimization

Use LSTM-based forecasting to predict steam/electricity demands
Automatically feed forecasts into MILP solvers for plan generation
Maintain consistent alignment with production and energy goals

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Monitor and Reduce Energy Waste

Track energy use intensity across units and equipment
Identify abnormal patterns and inefficiencies in real time
Generate daily, weekly, and monthly reports for continuous improvement

Energy Data-Driven Operations

Visualize KPIs related to steam, power, cooling, etc.
Integrate with production data for deeper insights
Support energy audits and sustainability compliance

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Applying RL to MPC Control Tuning

Conventional Approach

Traditional RL(Reinforcement Learning)-based control methods attempt to let AI govern the entire plant operation, which poses significant risks when applied directly to real facilities.
Simulators used for RL training rarely replicate real-world plant conditions accurately.
Incorrectly learned policies can result in safety incidents or product quality issues.

Infotrol’s Approach

Maintains the existing stable control systems (HIECON, HPCB) as-is
RL operates as an auxiliary tuning module, adjusting only specific parameters (e.g., setpoint trajectory)
Uses proven RL algorithms such as Soft Actor-Critic and PPO (actor–critic based)
Trains on historical and simulated plant data, then validates in real-time with actual process feedback

Why This Approach Matters?

Unlike replacing control systems, Infotrol AI enhances them with safe, modular AI components – ensuring compatibility and stability
Applying AI alone increases operational risk; Infotrol minimizes this by focusing on safe, stepwise enhancement
Combines past data and simulators with real-world feedback to ensure both performance and safety
The goal is not to replace existing systems, but to intelligently augment proven technologies with AI

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