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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

Infotrol technology

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

Infotrol technology

Integrated Solution Architecture

Infotrol ai

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

Infotrol technology

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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Infotrol ai

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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Real-World Applications

Vision AI

Filtration Process Automation

Detects filtration completion via camera input
Automatically opens transfer valves
Result: 95% reduction in manual operations

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Vision AI

Level & Emulsion Detection

Predicts fluid levels in real time from video
Operates without sensors, even in visually obstructed environments
Integrated with DCS for real-time alerts

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Predictive AI

Heat Demand Forecasting

Models seasonal and daily usage patterns
Enables MILP-based energy supply planning
Reduces manual planning effort and energy cost

Control AI

MPC Parameter Auto-Tuning

RL module adjusts tuning parameters only
Enhances control accuracy without replacing existing systems
Uses actual plant data + simulators for training

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Why Infotrol AI is Different

🧠 AI is a smart enhancement

AI doesn't replace control systems — it enhances decision-making and responsiveness.

🏗️ Built on proven systems

Integrated with Infotrol’s reliable control platforms like IPOES and HIECON.

🧪 Field-tested and practical

Applied in real plant environments with measurable performance gains.

⚖️ Safe and balanced

Combines AI innovation with operational safety for industrial-grade deployment.

Infotrol research project

Projects & Patents

2023.04.01 ~ 2026.12.31

Development and Demonstration of Community Energy Management System (CEMS) for Demand-Based Energy Efficiency

2020.05.01 ~ 2022.12.31

Development and field verification of customized FEMS technology for high energy processes

2019.10.01 ~ 2023.09.30

Development of Smart ZEC Energy Trading Platform Operation Technology

특허 제 10-1532580호

Optimization System of Energy Network

본 발명의 일실시 예에 따라 비주얼 모델링 (Visual modeling) 환경에서 에너지 네트워크를 구성하고, 이를 이용하여 에너지 밸런스, 시뮬레이션 및 최적화를 수행하는 에너지 네트워크 최적화 시스템은 에너지 밸런스와 최적화를 수행하면서 내부 변수 관리, 스크립트 계산을 하고, 정보를 관리하는 ENetOPT메인모듈과 엔탈피, 에너지 밸런스, 최적화를 연산하는 연산모듈을 포함하는 ENetOPT부, ENetOPT부에 연결되어 사용자 UI를 제공하면서 다이어그램 디자인 기능을 제공하는 Visio부, ENetOPT부로부터 엔탈피, 에너지 밸런스, 최적화에 대한 데이터를 제공받아 저장하면서 모니터링하는 RDB부, 적어도 하나 이상의 장비에 인터페이스를 제공하는 RTDB부 및 ENetOPT부를 RTDB부와 연결시키는 중계 역할을 하는 Data Access Interface부를 포함한다.

Infotrol Technology