Automations
This pillar covers industrial process workflows that monitor stack emissions, optimize combustion parameters, and adjust carbon capture equipment based on real-time signals. Content should explore reinforcement-driven control loops, maintenance forecasting, and compliance reporting architectures for plants under increasing environmental and operating efficiency pressure.
This foundational page details a custom, end-to-end orchestration workflow that integrates real-time sensor data, predictive models, and control systems to autonomously manage emissions and carbon capture operations. It explains the architecture for agentic decision-making across monitoring, optimization, and compliance, delivering measurable reductions in operational cost, regulatory risk, and carbon intensity for industrial plants.
This page covers a custom workflow where specialized agents ingest and validate data from CEMS and IoT sensors to provide continuous, audit-ready emissions visibility. It details the architecture for anomaly detection, drift correction, and alerting, which reduces manual data validation effort and prevents compliance excursions by catching issues before they escalate.
This page explains a custom orchestration system that coordinates autonomous drones equipped with optical gas imaging to patrol facility perimeters, detect leaks, and geo-tag findings. The workflow automates inspection scheduling, data analysis, and work order generation, significantly reducing manual survey costs and improving leak detection rates for methane and VOCs.
This page details a reinforcement learning-driven workflow that continuously adjusts air-to-fuel ratios, burner positions, and load setpoints to minimize NOx/SOx while maximizing efficiency. It covers the integration with DCS/PLC systems, safety interlocks, and the architecture for real-time optimization that delivers fuel savings and lower emissions without compromising process stability.
This page outlines a custom multi-agent system that models the emissions and economic impact of alternative fuel blends (e.g., hydrogen, biomass, natural gas) in real-time. The workflow ingests spot market prices, fuel specs, and emission factors to recommend or automatically adjust blend ratios, reducing fuel cost and carbon footprint simultaneously.
This page describes a custom predictive control workflow that uses forward-looking models of process conditions to pre-adjust SCR/SNCR reagent injection or furnace parameters. It explains the architecture for integrating with advanced process control systems to reduce reagent consumption and avoid peak formation, lowering operational cost and ensuring permit compliance.
This page details a custom AI workflow that autonomously manages the energy-intensive solvent regeneration cycle in amine-based carbon capture plants. By optimizing stripper temperature, pressure, and steam use based on real-time flue gas conditions, the system reduces the capture energy penalty by 10-20%, directly improving project economics.
This page explains a custom orchestration layer where agents dynamically balance carbon capture rate against plant energy consumption and electricity market prices. The architecture integrates with plant historians and market data APIs to make minute-by-minute setpoint decisions, maximizing carbon abatement value or operational profit under fluctuating conditions.
This page covers a custom workflow that analyzes vibration, temperature, and performance telemetry from critical CCUS infrastructure to forecast failures. It details the agentic architecture for diagnosing issues, ordering parts, and scheduling maintenance during planned outages, preventing unplanned downtime that could halt capture operations or CO2 offtake.
This page describes a custom automation workflow where AI agents monitor gas chromatograph and moisture analyzer data to ensure captured CO2 meets pipeline or storage specifications. It triggers automatic diversion to polishing units or alerts operators, safeguarding product quality and preventing costly contamination of storage reservoirs or utilization processes.
This page outlines a custom control workflow for DAC facilities, where agents adjust fan speeds, sorbent cycling, and regeneration energy based on ambient humidity, temperature, and renewable power availability. The architecture reduces levelized cost of capture by optimizing the highly variable energy profile of DAC against real-time constraints.
This page details a custom workflow that integrates downhole pressure sensors, seismic monitoring data, and reservoir models to autonomously adjust CO2 injection rates. It ensures safe plume migration and maximizes storage capacity while preventing over-pressurization, requiring a robust architecture for data fusion and control with human-in-the-loop approvals for critical changes.
This page explains a custom workflow that automates the end-to-end compliance reporting process, from data collection and validation to form filling and submission. It covers the architecture for integrating with CEMS, lab data, and ERP systems, along with audit trails and approval gates, reducing reporting labor by over 80% and eliminating manual errors.
This page describes a real-time agentic workflow that compares live emissions data against complex permit conditions (e.g., rolling averages, fuel-specific limits) and triggers alerts or corrective actions before violations occur. It details the rule engine and integration with control systems needed for proactive compliance management in heavily regulated industries.
This page covers a custom workflow that automates the measurement, reporting, and verification (MRV) process for carbon credits. Agents collect sensor data, apply protocol methodologies, generate verification-ready documentation, and even interface with registry APIs, accelerating credit issuance and reducing the administrative cost of participating in carbon markets.
This page outlines a custom orchestration workflow that pulls emissions data from fragmented sources—plant historians, utility bills, procurement systems, and supplier portals—to build comprehensive, granular carbon inventories. It explains the architecture for data mapping, gap filling, and auditability, turning a quarterly manual struggle into a continuous, automated process.
This page details a custom AI workflow that predicts failures in Flue Gas Desulfurization units, Electrostatic Precipitators, and baghouses by analyzing pressure drops, power consumption, and particulate readings. It automates work order generation and parts procurement, preventing catastrophic failures that lead to emissions spikes and forced outages.
This page explains a multi-agent workflow where AI diagnoses faulty sensors or control valves in carbon capture plants by cross-referencing readings with physical models and historical patterns. It can trigger automatic recalibration, switch to redundant instruments, or create precise maintenance tickets, minimizing instrument-induced downtime and data integrity issues.
This page covers a custom workflow that closes the loop from anomaly detection to field action. When an emissions or equipment fault is detected, agents automatically generate detailed work orders in the CMMS, check crew availability and certifications, and dispatch tasks with relevant SOPs and safety data, drastically reducing mean time to repair.
This page describes a custom workflow where a physics-informed digital twin of the emissions control system is used to simulate maintenance impacts and optimize scheduling. Agents run 'what-if' scenarios to find maintenance windows that minimize emissions spikes or production loss, then sync the optimized schedule with the real-world CMMS and operator logs.
This page details a custom automation workflow that allows carbon capture plants to participate in grid demand response programs. Agents modulate capture unit energy consumption based on real-time electricity prices or grid operator signals, creating a new revenue stream while managing the impact on capture rates through predictive buffering strategies.
This page explains a workflow where agents ingest live grid carbon intensity data and adjust plant processes (e.g., shifting compression, running capture units) to minimize the facility's overall carbon footprint. This architecture supports corporate carbon accounting goals and can be integrated with renewable power purchase agreements for maximum impact.
This page outlines a custom orchestration workflow that aligns the variable output of on-site solar or wind assets with the flexible load of a carbon capture plant. Using weather forecasts and market signals, agents schedule capture and compression to run during periods of high renewable generation, lowering the net carbon intensity and energy cost of capture.
This page covers a custom multi-agent workflow that evaluates supplier ESG scores, real-time carbon footprints, and spot prices to recommend or automatically execute purchases of green hydrogen, biogas, or sustainable biomass. It integrates with procurement and inventory systems to optimize blends that meet cost and emissions targets.
This page details a workflow where AI agents attach a dynamic carbon footprint to every raw material delivery by ingesting supplier-specific production data, transportation mode, and distance. This creates a live carbon inventory for production planning and Scope 3 reporting, moving beyond annual average emission factors to granular, actionable data.
This page describes a custom automation workflow that collects activity data (e.g., fuel use, production volumes) from key suppliers via APIs or parsed reports, applies relevant emission factors, and models decarbonization scenarios. It transforms Scope 3 accounting from a static annual exercise into a dynamic tool for supplier engagement and risk management.
This page outlines an industry-specific workflow for cement plants, where AI agents optimize the clinker-to-cement ratio and blend of alternative fuels (tires, waste) based on real-time kiln conditions and emissions. The architecture integrates with quality control systems to maintain product specs while maximizing fuel substitution and reducing process CO2.
This page details a custom workflow for integrated steel mills, where agents optimize the recovery and use of blast furnace gas (BFG) between power generation, heating, and new carbon capture units. It makes real-time decisions on gas routing to maximize value and carbon abatement, a complex optimization problem critical to steel decarbonization.
This page covers a refinery-specific automation workflow focused on Fluid Catalytic Cracking Units (FCCU) and hydrotreaters. Agents adjust catalyst injection, reactor temperatures, and treat gas rates to minimize SOx, NOx, and CO emissions while protecting yield, integrating with advanced process control layers for closed-loop optimization.
This page explains a custom workflow for aluminum smelters that uses AI to predict and prevent anode effects, the primary source of potent PFC emissions. By analyzing cell voltage, alumina feed rates, and bath chemistry, agents can trigger corrective actions to maintain stable operation, drastically reducing PFC emissions and associated carbon credit liabilities.
This page describes the architecture for a custom, high-fidelity digital twin that simulates the entire emissions control system—from boiler to stack. The workflow uses live data to calibrate the twin, enabling agents to run optimization and predictive maintenance scenarios that are too risky or slow to test on the physical plant.
This page details a workflow where plant engineers and operators can use a conversational AI interface to ask 'what-if' questions about process changes (e.g., new fuel, equipment shutdown). Agents query the digital twin, run simulations, and return impact reports on emissions and efficiency, accelerating decision-making and de-risking operational trials.
This page covers an advanced workflow where reinforcement learning agents explore the digital twin's operating space to discover non-intuitive setpoints that lower emissions or energy use beyond human expertise. These regimes are then validated and cautiously implemented in the physical plant, with robust safety interlocks and oversight.
This page explains a safety-critical automation workflow that fuses data from area gas detectors, wind sensors, and process readings to model dispersion and detect leaks earlier than individual sensors. It triggers alarms, ventilation controls, and emergency shutdown sequences, improving plant safety and preventing incidents that could lead to major emissions events.
This page details a workflow for emergency response where, upon detecting a toxic release, agents automatically run real-time atmospheric dispersion models using weather data. The system then triggers internal alarms and can generate pre-formatted alerts for community warning systems, ensuring a faster, more accurate response to protect public health.
This page covers a workflow that automates the labor-intensive process of incident investigation. After an emissions exceedance or safety event, agents compile time-series data, operator logs, and maintenance records, apply root-cause analysis frameworks, and draft preliminary investigation reports, compressing a days-long process into hours.
This page outlines a financial automation workflow for organizations active in carbon markets. Agents monitor project performance, track verification status, model future credit issuance, and even execute trades based on portfolio strategy and price signals, treating carbon credits as a managed asset class to maximize value and hedging.
This page describes a workflow that implements an internal carbon price by automatically calculating a shadow cost on all operational emissions. Agents apply this cost to real-time production decisions, investment evaluations, and budgeting processes, embedding carbon accountability directly into financial and operational planning systems.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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