If industrial system upgrades are understood simply as “bigger equipment and higher parameters,” a critical fact is often overlooked: what truly determines long-term system stability is not hardware, but control logic.

Against the backdrop of the ongoing “dual-carbon” goals, foundational systems such as industrial water supply—characterized by high energy consumption and continuous operation—are facing dual pressure for both energy efficiency and stability. Pumps cannot stop, pressure cannot fluctuate, and energy consumption must still be reduced. To be honest, this is not a new problem, but it has never been fully solved.

It is precisely under these real-world constraints that the COSMOPlat AI Constant-Pressure Algorithm entered the industry’s field of view. Instead of following the traditional path of “adding more sensors,” it chose a more difficult—but more valuable—direction: redefining constant-pressure control itself through algorithms.

I. The Problem Is Not New, but It Has Never Been Truly Solved

In scenarios such as district heating, circulating water systems, and process cooling, the importance of constant-pressure control needs no elaboration.

However, anyone who has worked on site knows the awkward reality:

PID control, though theoretically mature, often fails to behave “obediently” in industrial environments.

The reason is straightforward.
Pressure sensors operate long-term in high-temperature, high-humidity, and high-interference environments, with an average failure rate of about 12%. This is not an extreme case—it is the norm.

Once sensor drift occurs, pressure fluctuations are amplified. In heating systems, fluctuations of ±0.5 MPa are not uncommon, leading to noise, vibration, and even forced shutdowns.

Cost is an even more practical issue. Calibration, replacement, and maintenance—these “invisible” tasks—often account for around 20% of total operations and maintenance costs.

The root cause is not that the algorithm is “not complex enough,” but that the control logic relies too heavily on fragile hardware.
This is a structural problem.

II. A Different Perspective: Must Constant Pressure Depend on Sensors?

When designing the COSMOPlat AI Constant-Pressure Algorithm, the engineering team’s first step was not parameter tuning, but posing a seemingly counterintuitive question:

If there were no pressure sensors, could the system still maintain stable, constant pressure?

The answer was not “barely,” but rather: yes—and even more stably.

The core idea is clear:
Instead of endlessly improving sensor accuracy, reduce system-level dependence on sensors.

By deeply modeling the operating characteristics of pumps and motors, the COSMOPlat AI Constant-Pressure Algorithm replaces traditional physical feedback with data prediction and model inference. Constant-pressure control no longer relies entirely on external signals to “tell the system what has happened,” but instead allows the algorithm to actively judge “what will happen next.”

This is not a weakening of control, but a reconstruction of control logic.

III. Not Just an Algorithm, but an Integrated Control System

From an engineering implementation perspective, the COSMOPlat AI Constant-Pressure Algorithm is not a “black-box model,” but a clearly layered, three-stage architecture.

1. Perception Layer: No Longer “Measuring Pressure,” but “Observing the Motor.”
The system no longer introduces additional pressure sensors; instead, it directly captures the harmonic features of the motor’s three-phase current.
By analyzing stator resistance and rotor time constants, a digital model of electromagnetic torque is constructed, providing a continuous and stable data foundation for subsequent calculations.
This approach may seem indirect, but in practice, it avoids the most failure-prone hardware nodes.

2. Algorithm Layer: Prediction Rather Than Passive Response
At the core algorithm level, the COSMOPlat AI Constant-Pressure Algorithm introduces an LSTM-based flow estimation model that covers multiple pipe diameters and varying medium viscosities, with dynamic identification accuracy controlled within ±2.5%.
Head prediction combines computational fluid dynamics models with dynamic pipe-resistance compensation. Even under frequent load fluctuations, system response time remains stable within 8 milliseconds.

There is only one keyword here: foresight.

3. Execution Layer: Control Commands Must Be Actionable
Even the best algorithm is worthless if it cannot be implemented at the execution layer.
On the control side, the system adopts an adaptive drive strategy, achieving valve control accuracy of 0.5 degrees.
Combined with harmonic suppression mechanisms, output torque at zero-speed startup can reach 120% of the rated value, significantly improving reliability under low-speed conditions.

IV. Engineering Details Are Where the Algorithm’s True Value Lies

Around the COSMOPlat AI Constant-Pressure Algorithm, a series of seemingly inconspicuous but critical engineering refinements were implemented:

Adoption of SiC power devices and elimination of electrolytic capacitors, extending drive system lifespan to 80,000 hours;

Combined harmonic and noise suppression, reducing electromagnetic noise in pump rooms by approximately 12 dB;

For retrofitting existing equipment, the introduction of a single-phase motor sinusoidal drive and flux-observer algorithms improves overall efficiency by about 7%.

These are not isolated optimizations, but systematic choices made with “long-term stable operation” as the core objective.

V. Data Does Not Lie: Field Validation Matters More Than Concepts

In real industrial projects, the COSMOPlat AI Constant-Pressure Algorithm has delivered clear, data-backed results:

Overall energy savings of pump systems reaching 14.7%;

Average daily electricity savings of approximately 83 kWh;

Pressure fluctuation range converged to ±0.02 MPa;

Mean time between failures increased to 45,000 hours;

Annual maintenance costs reduced by 62%, with sensor-related failures nearly eliminated.

These results demonstrate one simple truth:
When control authority is truly handed back to algorithms, the system becomes more “well-behaved.”

VI. From a Technical Choice to a Long-Term Trend

It must be acknowledged that, during early model training and parameter adaptation, the COSMOPlat AI Constant-Pressure Algorithm places higher demands on data accumulation and engineering expertise. It is not a “plug-and-play” solution.

However, from a full life-cycle perspective, the stability, energy efficiency, and scalability brought by algorithm-driven control far outweigh the initial investment.

More importantly, it validates a clear path:
Use algorithms to replace high-failure-rate hardware.

This approach applies not only to water supply systems but also provides a replicable engineering paradigm for the intelligent upgrading of fluid machinery and energy systems. As industrial data infrastructures continue to mature, intelligent water supply solutions built around the COSMOPlat AI Constant-Pressure Algorithm are driving industrial equipment from passive adjustment toward proactive prediction.