Petrochemicals

Business problem

• Multi-plant, multi-grade scheduling by spreadsheet leaves margin and capacity on the table; grade changeovers, tank constraints and energy tariffs are balanced manually.

AI solution

• AI/optimization schedulers balance demand, constraints, changeovers and energy tariffs across plants; reinforcement learning and decision intelligence for re-planning.

Business value

• Operational efficiency — higher capacity utilization and margin from better plan quality.

Business problem

• Crackers, FCCs, aromatics and derivative units run inside conservative envelopes; yield–severity–energy–run-length trade-offs shift with feed and prices faster than engineers can re-optimize.

AI solution

• Reinforcement-learning / deep-learning optimizers layered above APC/DCS learn plant behaviour from historian data and continuously write optimal setpoints toward an economic objective.

Business value

• Revenue growth — a 0.5–2% yield/margin uplift is worth millions per year on a world-scale unit.

Business problem

• Product quality (melt index, density, purity, RVP, sulfur) is confirmed by lab analyses hours apart; operators fly blind between samples, causing quality giveaway and off-spec production.

AI solution

• ML virtual analyzers (PLS, neural networks, LSTM/GRU) predict quality continuously from routine process measurements, for monitoring and inferential control.

Business value

• Operational efficiency — less giveaway and off-spec, faster grade certification; the most industrially penetrated ML application in process industries.

Business problem

• Fouling, exchanger degradation, instrument drift and developing upsets go unnoticed until trips, off-spec runs or flaring events occur.

AI solution

• Multivariate anomaly-detection and early-warning models on unit sensor data flag abnormal signatures hours-to-days ahead, with drill-down to contributing variables.

Business value

• Risk reduction — avoided trips and slowdowns, fewer flaring events, extended equipment life.

Business problem

• Coke deposition in cracker furnace coils forces periodic decokes; running too hard shortens run length and tube life, running soft sacrifices yield — the core economic trade-off of an ethylene plant.

AI solution

• ML models forecast tube-metal-temperature and coking trajectories per furnace, then optimize severity and decoke scheduling across the furnace fleet to maximize cumulative ethylene output.

Business value

• Revenue growth — yield and run-length gains worth millions annually per cracker; reduced energy and decoke frequency.

Business problem

• There is no safe way to test operating changes, revamps and debottlenecks before execution; engineering data is fragmented.

AI solution

• First-principles + ML hybrid twins synchronized to live data for what-if simulation, debottlenecking, revamps and operator training.

Business value

• Operational efficiency — de-risked operating changes, faster revamps and better-trained operators.

Business problem

• Unplanned failures of compressors, pumps, extruders, furnaces and exchangers cause multi-day outages — a single cracker train trip can cost millions plus flaring and safety exposure; time-based maintenance over-services healthy equipment.

AI solution

• ML models trained on vibration, temperature, pressure and process data score asset health, detect degradation weeks in advance and prioritize interventions; increasingly extended with AI-agent root-cause analysis.

Business value

• Cost reduction — availability gains, 10–20%+ maintenance-cost reduction and avoided catastrophic failures.

Business problem

• Manual safety observation cannot continuously cover PPE compliance, restricted-zone intrusion, vehicle–pedestrian conflicts and man-down events in high-hazard chemical sites.

AI solution

• CV models on existing CCTV detect missing PPE (incl. respirators/gloves), unsafe forklift behaviour, zone breaches and collapsed workers, with real-time alerts and trend dashboards.

Business value

• Safety — measurable incident and violation reduction; strong, fast ROI.

Business problem

• Energy is 40–60%+ of cracker cash cost and >90% of cracker CO₂ comes from furnace energy demand; steam systems, furnaces and utilities run off optimum.

AI solution

• ML + optimization of furnace firing, steam let-down/balance, boiler load allocation and site utilities; energy-aware scheduling.

Business value

• Cost reduction — direct fuel savings plus CO₂ reduction supporting compliance and net-zero targets.

Business problem

• Chemical demand is cyclical, sparse at grade–customer level and whipsawed by destocking cycles; poor forecasts inflate inventory and cause allocation failures.

AI solution

• Hierarchical ML time-series forecasting across product/region/customer hierarchies, blending order history with market and macro signals; AutoML for scale.

Business value

• Operational efficiency — improved service levels, lower working capital, better S&OP.

Business problem

• Developing new formulations, polymers, catalysts and additives takes 12–24+ months of trial-and-error; specialty margins depend on faster innovation and reformulation for sustainability rules.

AI solution

• Predictive and generative models trained on decades of proprietary formulation data propose candidates and predict properties; chemical foundation models and agentic lab workflows cut experimental cycles.

Business value

• Revenue growth — faster time-to-market for higher-margin products.

Business problem

• Operating knowledge is scattered across SOPs, P&IDs, MOC records, shift logs and retiring experts' heads; engineers spend hours hunting documents and onboarding is slow.

AI solution

• Secure LLM assistants with RAG/GraphRAG over plant-specific documents give cited answers to operators, maintenance and engineers; enterprise copilots for all staff.

Business value

• Operational efficiency — broad productivity gains, faster troubleshooting, knowledge retention.