Reshaping the Dimension of Management: From “Passive Equipment” to AI “Smart Nodes”

As global industry advances toward digital transformation and ESG sustainable governance, energy management in high-energy-consuming facilities has become the core battlefield for enterprises to achieve net-zero emissions. Jointly developed by TAI-TSENG and UCCT, the iECO-CTS Cooling Tower Smart Energy-Saving Platform aims to reshape the management logic of industrial cooling systems.
Driven by its core AI engine, the platform establishes an automated cycle of “Perception, Judgment, Action, and Learning.” It successfully breaks through the traditional cooling tower limitations of “passive operation and non-transparent information,” helping high-tech manufacturing plants achieve significant energy savings of 15%–30%. Furthermore, it translates these energy-saving performances into credible, tangible ESG data assets.

Reshaping the Dimension of Management: From “Passive Equipment” to AI “Smart Nodes”

In industrial cooling systems, although the energy consumption of cooling towers accounts for less than 10% of the total, it is the critical node that determines the Coefficient of Performance ($COP$) of the entire chiller unit. If poor cooling efficiency leads to abnormal condensation temperatures, it will not only severely degrade the efficiency of the main chiller but also threaten the stability of precision manufacturing processes. However, traditional management models mostly rely on rules of thumb without data support (blind maintenance) and basic start-stop controls. This not only increases the risk of unplanned downtime but also conceals a massive potential for energy efficiency improvements.

The core value of iECO-CTS lies in empowering equipment with “cognitive and decision-making capabilities.” Through a four-stage intelligent evolution, it reshapes cooling towers into strategically significant “active energy-saving nodes”:

1. Data Perception: By deploying high-precision sensor matrices, the platform captures real-time data on temperature difference, flow rate, water pressure, environmental wet-bulb temperature, and mechanical/electrical health status (vibration, displacement, current), building a comprehensive visualized operational foundation.

2. AI Modeling & Analysis: By implementing AI deep learning algorithms to analyze historical and real-time data, the platform accurately predicts heat load trends for the next 1–3 hours. It dynamically calculates the optimal energy efficiency point (kW/RT) and generates recommendations for the best cooling parameters.

3. Precision VFD Regulation: Integrating Variable Frequency Drive (VFD) control technology, the system evolves the energy supply mode from “fixed power” to “on-demand regulation.” This maximizes energy-saving performance while ensuring process safety as a prerequisite.

4. Continuous Self-Evolution: Through data cleansing and ongoing model annotation optimization, the platform implements predictive maintenance and fault early-warning mechanisms. This ensures the system continuously enhances its intelligence over time during operation, achieving the governance goals of “high efficiency, stability, and transparency.”

Core Twin AI Engines: Balancing Safety & Stability with Energy-Saving Performance

The technical foundation of iECO-CTS is built upon a collaborative architecture of “Twin AI Engines,” designed to secure the high stability of manufacturing process safety while simultaneously ensuring exceptional energy-saving performance. The system leverages synergy through two major core engines:

1. Energy Optimization Engine：It dynamically integrates environmental temperature and humidity parameters to accurately predict heat load variations, thereby generating recommendations for the fan’s optimal operating frequency and speed, helping enterprises fulfill their precision carbon reduction targets.Predictive

2. Maintenance Engine：Featuring 18 built-in fault prediction models, this engine performs in-depth diagnostics on rotating equipment and mechanical/electrical health status. It assists management teams in identifying potential risks ahead of time, effectively eliminating losses caused by unplanned downtime.

To meet stringent industrial safety standards, the system introduces an “Active Safety Control” mechanism. The optimization recommendations generated by AI algorithms are prioritized and fed back to the SCADA system, allowing the owner to evaluate and decide whether to adopt them based on on-site practical conditions.
While ensuring the absolute safety of the manufacturing process environment, this mechanism further guarantees that on-site engineers retain the core decision-making and management rights over critical equipment.

Leading Smart Governance in Industrial Cooling: Empowering Sustainable Competitiveness via Data Assets

iECO-CTS is custom-built for high-energy-consuming environments, such as high-tech manufacturing plants and large commercial office centers. Its technical architecture spans precision sensing and edge gateway integration to cloud AI computing, providing a one-stop data visualization center and intelligent analytical reports. The system introduces a proprietary “0-100 Health Scoring” mechanism that deeply integrates temperature control efficiency, energy load, and equipment health indicators. Through intuitive status indicator lights, it ensures that every maintenance decision is backed by solid data evidence.

Amid the wave of ESG sustainable governance, iECO-CTS further positions itself as an “ESG Data Source Platform” for enterprises. It can generate real-time and precise “energy-saving and carbon-reduction data” along with “quantifiable electricity-saving indicators,” serving as the most robust raw data foundation when compiling sustainability reports or applying for international certifications. We not only lead cooling systems toward digital transformation but also assist enterprises in converting energy-saving performance into tangible data assets, unlocking a new future of smart industrial governance.

Tel: (06)583-2198 #32 | Mail : adongao@yuchens.com
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