"Chemical companies always fall into a dilemma when it comes to AI — chasing safety lowers efficiency, while pushing for more output raises energy use. Yet Shandong Haihua used AI to turn that dilemma into a win-win."
As AI reshapes the paradigm of industrial development, deeply integrating AI has become an irreversible core path for the chemical industry to break through its traditional bottlenecks of "safety compliance vs. efficiency, cost control vs. quality stability, and continuous production vs. equipment maintenance." As a leader in China's salt-chemical sector, Shandong Haihua Group was among the first to define a clear transformation direction — "data as a new factor of production, AI as a systematic driver." Rather than chasing technological spectacle, it focused on the industry's essential contradictions, and by partnering with Inspur Digital Enterprise to deploy a large salt-chemical intelligent-control model, achieved the win-win of "safer and more efficient operations, lower cost and higher quality."
Public data shows that the chlor-alkali plant of Shandong Haihua Group, through its salt-chemical intelligent-control model built with Inspur Digital Enterprise, saved 4.5 million kWh of electricity per year, extended the service life of its ion-exchange membranes from 4 to 5 years, and improved safety inspection efficiency by 50%. These results did not come from "technological spectacle," but were the inevitable outcome of precisely resolving the three essential contradictions of "safety, efficiency, and quality" in the salt-chemical industry.
01 Three Major Business Conflicts in Salt-Chemical Production
As a capital-intensive, high-risk industry, salt-chemical production can never escape its three core objectives of "safety compliance, resource conversion, and production continuity." Yet under traditional models, the conflicts among these objectives have become an industry-wide ailment.
1. The "Rigid Constraints" of Safety Compliance vs. the "Practical Bottleneck" of Inspection Efficiency
Salt-chemical plants are filled with high-risk areas such as electrolyzers and chlorine gas pipelines. National work-safety regulations require a 100% completion rate for hazard screening, yet manual inspection struggles to meet this. Shandong Haihua's traditional inspections relied on "shift rotation plus human observation," and at night, fatigue drove up the missed-inspection rate significantly.
Hazardous-process detection is the primary scenario for deploying chemical AI; industry-wide, over 60% of safety accidents stem from the "spatiotemporal blind spots" and "slow response" of manual inspection.
2. The "Cost-Reduction Demand" of Resource Conversion vs. the "Stability Requirement" of Product Quality
Raw salt and electricity account for over 50% of salt-chemical production costs, so cost reduction must start with "efficient resource conversion." Yet traditional manual parameter tuning pits "cost reduction" against "quality." At Shandong Haihua's chlor-alkali plant, electrolyzer parameter adjustment depended on veteran technicians' experience; to guarantee product purity, the plant had to maintain high redundant energy consumption. Meanwhile, unstable parameters accelerated ion-exchange membrane aging, with a traditional service life of only 4 years. This dilemma of "sacrificing energy for quality" is widespread across the industry.
Intelligent production-process optimization is the core scenario for chemical AI precisely because manual adjustment struggles to balance the triangle of "energy consumption, purity, and equipment life."
3. The "Capacity Goal" of Continuous Production vs. the "Downtime Risk" of Equipment Maintenance
Salt-chemical lines must run continuously 24 hours a day. Industry data shows that a single unplanned hour of downtime on one chlor-alkali line causes direct losses exceeding ¥100,000. Yet Shandong Haihua's traditional maintenance followed a "repair-after-failure plus periodic shutdown inspection" model, leaving many failures impossible to predict in advance. Periodic inspection reduces failures but requires halting production, and the annual capacity lost to such maintenance is substantial.
02 Where AI Lands: Not Replacing People, but Resolving Traditional Conflicts
The salt-chemical intelligent-control model (Ch1 version) developed by Shandong Haihua with Inspur Digital Enterprise did not aim for "full-process labor replacement." Instead, targeting the three contradictions above, it used "scenario-based technology" to fill the capability gaps of traditional models.
1. Safety vs. Efficiency: A multimodal inspection agent that lifts "compliance" and "efficiency" together
Addressing the pain points of "incomplete manual inspection coverage and slow response," the two parties built a multimodal inspection agent that delivers a full closed loop of "perception, analysis, decision, and action," ensuring "second-level warning" for high-risk hazards.
2. Cost Reduction vs. Quality: A dual-driven process-optimization agent that delivers a win-win of "energy consumption" and "equipment life"
During the informatization stage of the chemical industry, production parameters had been digitally upgraded, but the data remained stuck at the static level of "passive recording": it could only store historical values, unable to correlate the linkage of "output, quality, and energy consumption," let alone proactively guide production adjustments. As a result, when technicians tuned parameters manually, key indicators often fluctuated due to lag.
The AI-era process-optimization agent breaks this limitation completely. The agent built by Shandong Haihua Group with Inspur Digital Enterprise, relying on multi-objective dynamic optimization algorithms and machine-learning models, achieves "minute-level autonomous optimization" of process paths: it integrates real-time data such as raw-salt purity, cell temperature, and current, automatically balancing the multi-dimensional demands of output, quality, energy consumption, and safety. This turns the previously volatile curves of key production indicators (such as electrolyzer voltage) from "ECG-like" fluctuations into an all-weather steady "straight line."
3. Continuity vs. Maintenance: A predictive-maintenance agent that balances "capacity" and "maintenance"
Chemical production equipment operates year-round under harsh conditions of high temperature, high pressure, and strong corrosion. Traditional maintenance always faces a double bind: over-maintenance drives up operating costs, while unpredicted failures trigger unplanned downtime that directly disrupts production continuity.
To break this deadlock, Shandong Haihua Group built an equipment predictive-maintenance agent and established an integrated intelligent operations platform of "data perception + failure mechanisms + AI reasoning" — no longer relying on "repair-after-failure" or "periodic shutdown inspection," but shifting maintenance from "passive repair" to "active early warning," and upgrading from "planned maintenance" to "predictive maintenance."
The platform deeply integrates equipment failure mechanisms with multi-source operational data, building a dynamic health profile for equipment based on key operating indicators such as vibration, temperature, and pressure. Using algorithms like time-series analysis, life prediction, and root-cause diagnosis, it evaluates the real-time status of critical equipment, identifies potential risks in advance, and prevents hazards from escalating at the source. Its fault-identification accuracy exceeds 95%, fortifying both production continuity and intrinsic safety with an intelligent defense, while driving equipment management from a traditional "cost center" toward a "value center" that creates value.
03 Lessons from the Case: Proving AI Is Productivity Through Results
Shandong Haihua's practice breaks many fixed perceptions of AI in the chemical industry and offers traditional chemical enterprises a replicable path to adoption.
1. "Decomposing Contradictions" Matters More Than "Choosing Technology"
When many chemical companies adopt AI, they first agonize over "choosing a hundred-billion-parameter model or a dedicated algorithm," yet overlook "finding the industry's essential contradictions first." Shandong Haihua's logic is the reverse: first clarify contradictions such as "safety vs. efficiency" and "cost reduction vs. quality," then selectively adopt fitting technologies like "edge computing and multimodal recognition" — for example, using edge nodes rather than full cloud hosting to achieve "real-time warning," and customizing anti-interference sensors for the corrosive environment rather than buying off-the-shelf generic equipment.
2. "Human-Machine Collaboration" Is More Realistic Than "Unmanned Replacement"
The chemical industry often worries that "AI will replace workers," but Shandong Haihua's practice shows that AI is a "complement" rather than a "replacement." Safety officers shift from "repetitive inspection" to "hazard-response decisions," and technicians move from "manual tuning" to "process-optimization innovation," making human value stand out even more.
3. "Industry Benchmark" Is More Valuable Than "Technical Parameters"
Shandong Haihua's AI system does not boast about its "model parameter scale." The core competitiveness of chemical AI is not "how advanced the technology is," but "whether it solves the industry's real problems." When AI can make electrolyzers both save power and last longer, and make inspection both compliant and efficient, it naturally becomes a benchmark recognized by the industry.
The value of chemical AI has never been to "make factories smart," but to use technology to untie the industry's Gordian knot of "safety vs. efficiency, cost reduction vs. quality." Shandong Haihua's case proves that traditional chemical enterprises need not pursue "high-end" technology; by closely addressing their own business contradictions and letting AI precisely fill the gaps, they can turn the "dilemma" into a "win-win" — and that is the key to truly integrating AI into chemical productivity.
