A system and method for predictive management of industrial flares using a digital twin and blockchain based compliance reporting

Abstract

A system and method for the predictive management and automated compliance of industrial flares are disclosed. The system integrates multi modal data from optical and thermal cameras, alongside physical sensors, to create a real time digital twin of the flare stack. An artificial intelligence engine analyzes this data to perform predictive analysis, detecting anomalies such as incomplete combustion, gas leaks, and structural degradation, and forecasting potential failures. This enables predictive maintenance, optimizing operational efficiency and safety. The system features a secure, blockchain powered module for generating tamper proof, auditable compliance reports aligned with environmental regulations. It also includes an automated alert system, a comprehensive management dashboard, and advanced analytical reporting. This integrated approach provides a holistic solution for flare management, moving beyond simple monitoring to proactive and secure operational control.

Description

A System and Method for Predictive Management of Industrial Flares Using a Digital Twin and Blockchain Based Compliance Reporting

[0001] Addressing the fragmentation and reactive nature of existing flare-monitoring solutions, this predictive flare management system integrates multi-modal sensing high resolution optical and thermal imaging with gas, pressure, and flow instrumentation with an AI driven real time digital twin to forecast failures (e.g., flare tip degradation, structural risks, and combustion inefficiencies) before they occur. The system issues proactive, diagnostics-rich alerts and recommended actions, shifting operations from after-the-fact detection to preventative control. To guarantee audit-grade trust, all operational logs and generated compliance reports are cryptographically hashed and anchored to a permissioned blockchain, creating immutable, verifiable records suitable for regulatory scrutiny.

[0002] Key features include: (i) synchronized multi-modal data acquisition and edge preprocessing that fuses video / IR streams with industrial sensor data; (ii) a Predictive Digital Twin Core combining vision models (for flame / smoke segmentation) and time-series forecasting to estimate event probabilities with low false positive rates; (iii) a Compliance Trust Layer that secures data integrity via cryptographic hashing and blockchain anchoring, with optional zero knowledge verification to prove correctness without exposing raw data; (iv) one click automated reporting aligned to applicable thresholds and formats, attaching on-chain proofs to each report; (v) scalable integration with DCS / SCADA for closed-loop adjustments; and (vi) collaboration dashboards and ROI analytics quantifying emissions reduction and avoided downtime. Together, these components deliver safer, lower cost operations with tamper evident, audit ready compliance.

[0003] F23G7 / 08

[0004] JP2024008990A - Monitoring device, monitoring system and monitoring method.

[0005] To provide a monitoring device that estimates the flow rate of flare gas released from a flare stack. [Solution] The monitoring device includes an image acquisition unit that acquires an image of a flare, an image analysis unit that classifies each pixel included in the image into either a first pixel close to the flare pixel or a second pixel close to the background pixel based on the brightness value of a specific flare pixel that captures the flare and the brightness value of a specific background pixel that captures something other than the flare, and calculates the total number of pixels classified into the first pixel, a flare gas flow rate estimation unit that estimates the flow rate of the flare gas based on the total number of pixels, and an output unit that compares the flow rate of the flare gas with a specific threshold and issues an alarm.

[0006] US20230272910A1 - Flare monitoring system and method.

[0007] A flare monitoring system includes a camera configured to capture one or more images of a burning portion of a hydrocarbon gas emitted from a flare stack, memory circuitry storing instructions thereon, and processing circuitry configured to execute the instructions to estimate a flow rate of the hydrocarbon gas based on first data corresponding to the one or more images, and based on second data corresponding to an internal diameter of the flare stack.

[0008] US20210372613A1 - Apparatus for monitoring level of assist gas to industrial flare.

[0009] A remote sensing system which may be assembled with an Infrared (IR) sensor, or a plurality of IR sensors, disposed to sense IR radiance emitted as combustion products from a flare stack in two distinctive spectral bands, each band having a narrow spectral bandpass, the sensor being radiometrically calibrated to sense transmission characteristics of the two distinctive bands of the radiance from flare combustion gases; and an analyzer driven by a microcontroller, coupled to the IR sensor, to operationally respond in real time by generating an indication of flare stack's performance through a parameter derived from a ratio of the transmission characteristics of the two radiance outputs sensed by the IR sensor. The IR sensor of this flare monitoring apparatus must be positioned in such a way that the anticipated entire flame will be captured within the Field of View (FoV) of the IR sensor, or sensors.

[0010] US20220179399A1 - Method and System for Flare Stack Monitoring and Optimization.

[0011] An integrated and comprehensive method and system is disclosed for measuring and real-time monitoring of gas flare and using that information to improve and / or optimize oil and gas production and / or flare operations. A first embodiment of the invention comprises a camera or any other visual recognition and recording system coupled with an image and video analytics machine learning module to measure the flare and identify gas components or flow properties. A second embodiment of the invention is directed towards an intelligent optimization method and system that uses the flare and gas information and suggest a set of optimal production values to optimize flaring and reduce environmental impact of it.

[0012] US11519602B2 - Processes and systems for analyzing images of a flare burner.

[0013] Methods and systems for monitoring a flare burner with a camera. The methods and systems which may indicate to operators the presence or absence of one or more of smoke, flare flame, and steam plume and record those indications or measurements. Additionally, the methods and systems may confirm whether compliance with local regulations on visual emissions, smoke plume is achieved. The methods and systems automatically adjust the delivery rate of key inputs including measures assist fuel gas, purge gas, steam and / or air simultaneously to maintain or attain compliance with said local regulatory requirements. Also, methods for a machine learning process for using controller inputs to identify normal and abnormal flare states and provide visual indications and flare operation recommendations.

[0014] AU2008228846B2 - A flare characterization and control system.

[0015] A flare characterization system comprising: at least one camera for obtaining video images of a 5 flare emitted by a flare stack of a plant; a video analytics module connected to the at least one camera; a control system connected to the video analytics module; and 10 a user interface connected to the control system; wherein the flare characterization system is configured to store the obtained video images of the flare for later retrieval, review and / or regulatory purposes.

[0016] WO2024168206A1 - Flare stream classification with semantic segmentation and generative adversarial networks in normal and low light conditions.

[0017] Embodiments presented provide for a testing of flare streams. Classification of flare streams are performed by artificial intelligence in normal and low light conditions by processing visual data of the flare stream system in a computer arrangement to achieve visual data results and performing a postprocessing of the visual data results to produce postprocessing results.

[0018] CN118013197A - A flare flow monitoring emergency system and method based on deep learning algorithm.

[0019] The present invention relates to an emergency system and method combining flare flow measurement, flame detection, deep learning algorithm and linkage with government regulatory agencies, aiming to improve the accuracy and response speed of flare monitoring in chemical plants. The system consists of four main parts: a flare flow measurement module, a flame detection module, a deep learning algorithm processing unit and a government regulatory interface. The flare flow measurement module uses a high-precision sensor to collect data about the flare flow. The flame detection module monitors the state of the flame, including size, color and shape, through image recognition technology. These data are transmitted to the deep learning algorithm processing unit, which uses advanced deep convolutional neural networks (CNNs) to analyze the data and identify possible abnormal patterns or safety risks. A key innovation of the system is its real-time data sharing capability with government regulatory agencies, which ensures that relevant departments can be quickly notified and measures can be taken when potential risks are discovered. In addition, the system also includes an emergency response mechanism that can automatically start preset emergency processes such as alarm notification, safety valve activation and emergency shutdown procedures when abnormal conditions are detected. The comprehensive emergency system of the present invention not only improves the efficiency and accuracy of flare monitoring in chemical plants, but also enhances the processing capabilities in emergency situations through collaboration with government regulatory agencies, thereby significantly improving the safety management level of chemical plants.

[0020] Methods and systems for monitoring industrial flares are widely known in the prior art. Existing technologies have evolved from rudimentary systems based on image analysis, which estimate gas flow rates from pixel brightness or flame size , to more sophisticated approaches that combine visual data with physical parameters like the internal diameter of the flare stack. Furthermore, the use of specialized sensors, such as Infrared (IR) sensors, has been introduced to analyze combustion byproducts and assess the performance of assist gas. With technological advancements, more recent systems have begun to leverage artificial intelligence and machine learning for more complex analyses, process optimization, automated control of inputs to meet regulatory compliance , and even flare classification in adverse lighting conditions. Some of these systems also provide for communication with regulatory agencies during emergencies. However, these solutions are often fragmented, focusing on discrete aspects of monitoring. They generally lack an integrated, predictive, and fully secure framework for the holistic management of flaring operations and regulatory compliance.

[0021] The present invention aims to overcome these limitations by providing a comprehensive and intelligent platform that integrates several key innovations for the first time. The core of this invention is a Smart Digital Twin , which creates a real time, dynamic virtual replica of the entire flare system to enable simulation, process optimization, and, most importantly, predictive maintenance. This predictive capability extends beyond the reactive, rea time monitoring found in prior art systems. Furthermore, to guarantee the integrity and immutability of data for compliance reporting, this invention uniquely introduces an optional Blockchain layer to create secure, tamper proof, and auditable operational logs and reports. The system also dramatically simplifies the compliance process through one click automated reporting modules, which are specifically designed to generate audit ready reports tailored. This unique combination of a predictive digital twin, blockchain powered data integrity, and fully automated, localized compliance reporting provides a holistic solution not disclosed in the prior art.

[0022] The present invention is directed to a system and method that overcomes the limitations of the prior art by providing an integrated solution for the predictive management and secure, automated compliance of industrial flares.

[0023] In one aspect of the invention, a system is provided comprising: a multi modal data acquisition module for receiving data from optical, thermal, and physical sensors; and a processing circuitry. The processing circuitry is configured to execute instructions to: (a) generate a real time, dynamic digital twin of the flare system based on the acquired data; (b) utilize an artificial intelligence engine to perform predictive analysis on the digital twin, thereby forecasting potential operational failures, including but not limited to, material degradation, structural issues, and combustion inefficiencies; and (c) generate a secure, auditable compliance report, wherein the integrity of the report is ensured by anchoring a cryptographic hash of the report to a tamper proof, blockchain based ledger.

[0024] In another aspect, a method is provided that includes the steps of continuously acquiring multi-modal data from a flare system, constructing and updating a digital twin, performing predictive analysis to identify future risks, generating smart alerts for proactive maintenance, and automatically compiling and securing compliance reports on a blockchain.

[0025] The invention thus provides a technical solution that transitions flare management from a reactive monitoring paradigm to a proactive, predictive, and verifiable control paradigm, enhancing operational safety and providing a trustworthy mechanism for regulatory compliance.

[0026] The prior art in the field of industrial flare monitoring discloses various systems for real time observation and analysis of flare emissions. These systems, however, suffer from several significant technical limitations. Firstly, they are predominantly reactive, designed to detect anomalies like smoke or flame instability only as they occur. They lack a robust technical framework for proactively forecasting future operational failures, such as flare-tip degradation, structural integrity issues, or process inefficiencies, before they lead to costly shutdowns or safety hazards.

[0027] Secondly, while some systems employ artificial intelligence for data analysis, the data generated especially for regulatory reporting lacks inherent verifiability and integrity. Existing methods do not provide a mechanism to ensure that compliance reports and operational logs are immutable and tamper proof, which is a critical deficiency when facing stringent audits from regulatory bodies like the EU ETS or ILT.

[0028] Therefore, a technical problem exists in providing an integrated system that not only moves beyond reactive monitoring to predictive management of flare operations but also simultaneously solves the critical challenge of ensuring the trustworthiness and auditable integrity of the data it produces. There is a need for a unified solution that bridges the gap between predictive analytics and secure, verifiable compliance.Solution of problem

[0029] The present invention provides a comprehensive, integrated system and method for predictive flare management and automated, secure compliance reporting that addresses the key technical challenges in existing flare monitoring technologies. By combining multi-modal sensor data acquisition, a real time dynamic digital twin powered by artificial intelligence (AI) for predictive analytics, blockchain anchored data integrity, and one-click automated reporting tailored to regulatory formats, the system shifts flare operations from reactive monitoring to proactive control. This holistic approach not only forecasts potential failures to prevent costly shutdowns and safety risks but also ensures the verifiability and immutability of compliance data, overcoming the fragmentation and lack of trustworthiness in prior art systems. The following sections detail how the system's components and functionalities collaboratively solve the identified technical problems.

[0030] Solution 1: Transitioning from Reactive Monitoring to Proactive Predictive Management of Flare Operations

[0031] To overcome the reactive nature of prior art systems, which detect anomalies only as they occur, this invention employs a multi-modal data acquisition module and an AI powered digital twin to enable real time simulation and forecasting of operational failures, such as flare tip degradation, structural issues, and combustion inefficiencies.

[0032] Components:

[0033] Multi Modal Data Acquisition Module:

[0034] The foundation of the system's predictive capability is a robust data ingestion framework that collects continuous, synchronized streams from diverse sensors. This includes optical cameras (e.g., high resolution CMOS or CCD sensors with at least 1080p resolution and 30 fps frame rate) for capturing visible flame characteristics such as height, volume, shape, and color; thermal infrared (IR) cameras (e.g., dual band IR sensors with spectral ranges of 3-5 μm and 8-14 μm, calibrated for temperatures up to 2000°C) for thermography, heat mapping, and detection of flame detachment or pilot flame status; gas composition sensors (e.g., non dispersive infrared (NDIR) analyzers for CO₂, CH₄, NOₓ, and SO₂ detection with accuracy ±1% full scale); and physical sensors such as pressure transducers (e.g., piezoresistive sensors with 0-100 psi range) and flow meters (e.g., ultrasonic or thermal mass flow meters) integrated into the flare header and stack. These sensors are connected via industrial protocols like Modbus RTU or Ethernet / IP to an on premise edge computing unit (e.g., a ruggedized industrial PC with NVIDIA Jetson or equivalent GPU for local processing). Data is sampled at configurable rates (e.g., 1-10 Hz for physical sensors, 1-30 fps for cameras) and pre processed for noise reduction using techniques like Kalman filtering.

[0035] Smart Digital Twin for Process Optimization (Core Processing Engine):

[0036] The acquired data feeds into a real time virtual replica of the flare system, constructed using physics based modeling software (e.g., based on OpenSim or similar frameworks adapted for fluid dynamics and thermodynamics). The digital twin incorporates historical baseline data (e.g., collected over an initial 30 day learning period) to model normal operational states, including flame dynamics governed by equations such as the combustion efficiency index (CEI = [CO₂] / ([CO₂] + [CO] + [unburned hydrocarbons])) and structural integrity simulations using finite element analysis (FEA) for stress and corrosion modeling. The AI engine, utilizing deep learning models like convolutional neural networks (CNNs) for image analysis (e.g., semantic segmentation via U Net architecture trained on datasets of flare images under varying conditions, achieving >95% accuracy in flame / smoke detection) and recurrent neural networks (RNNs, e.g., LSTM) for time-series forecasting, analyzes deviations from the baseline. For instance, a subtle increase in flare-tip temperature (detected via thermal IR) correlated with gas composition shifts is forecasted using predictive models to estimate failure probability (e.g., via Monte Carlo simulations integrated with the digital twin, projecting a 75% risk of critical degradation within 14 days). This enables proactive alerts, such as medium priority notifications sent via SMS, email, or dashboard integration, including diagnostic graphs (e.g., thermal trend charts) and corrective suggestions (e.g., schedule flare tip replacement).

[0037] Forecast Event Module (AI-Based Prediction):

[0038] Integrated within the digital twin, this module allows configurable sensitivity thresholds (e.g., use defined via a web based interface) for predicting events like abnormal smoke increase, gas spikes (CO₂, CH₄, NOₓ, SO₂), or pilot flame extinction. Predictions are generated using ensemble AI models (e.g., combining CNN for visual data and gradient boosting machines like XGBoost for sensor fusion), trained on historical and simulated data to achieve low false positive rates (<5%).

[0039] Solution 2: Ensuring Immutable and Auditable Data Integrity for Compliance Reporting

[0040] Addressing the lack of verifiability in prior art systems, where operational logs and reports are susceptible to tampering, this invention introduces a blockchain powered layer to create secure, tamper proof ledgers for all data, ensuring trustworthiness during regulatory audits.

[0041] Components:

[0042] Blockchain Powered Data Integrity Module:

[0043] Operational data (e.g., sensor readings, AI predictions, event logs) is hashed using cryptographic algorithms (e.g., SHA 256) and anchored to a permissioned blockchain ledger (e.g., based on Hyperledger Fabric or Ethereum-compatible chains like Polygon for scalability). Each transaction (e.g., a compliance report generation) is timestamped and signed with private keys managed via role-based access control (RBAC), where users (e.g., operators, auditors) have predefined permissions (e.g., read-only for regulators). The ledger stores metadata such as event IDs, hashes, and timestamps, while raw data remains on secure, encrypted storage (e.g., AES 256 encrypted databases compliant with GDPR). This ensures immutability: once anchored, records cannot be altered without consensus from network nodes (e.g., distributed across facility servers and cloud backups). For ESG compliance tracking, the module generates transparent audit trails, verifiable by external parties like Dutch authorities (RIVM, ILT) through zero knowledge proofs (zk SNARKs) that confirm data integrity without revealing sensitive details.

[0044] Data Security Module:

[0045] Complementing blockchain, this includes RBAC for access (e.g., admin for configuration, viewer for dashboards), encryption in transit (TLS 1.3), and anomaly detection for unauthorized access attempts.

[0046] Solution 3: Automating Compliance and Reporting to Reduce Administrative Burden

[0047] To solve the inefficiencies in manual compliance workflows and fragmented reporting in prior art, the invention provides one-click, automated generation of audit-ready reports tailored to Dutch and EU formats, drastically reducing preparation time from 18 hours to under 10 minutes.

[0048] Components:

[0049] Compliance Module and Advanced Reporting:

[0050] This module monitors adherence to standards (e.g., IED, EU ETS, Dutch Environmental Management Act) by cross referencing real-time data against thresholds (e.g., emission limits for NOₓ <150 mg / Nm³). Violations trigger alerts, while periodic reports are auto-generated using templates (e.g., NEa or ILT formats). Reports include analytical displays (e.g., line charts for flame height over time, heat maps from thermography), event logs (e.g., anomaly detections with timestamps), and metrics like carbon equivalent index (CEI) or combustion index. Outputs are in PDF / Excel, with customizable analyses (e.g., filter by date range). The one click feature integrates with the dashboard: users select parameters, and the system compiles data from the digital twin, applies blockchain anchoring for proof, and exports the report.

[0051] Event Management and Notification Module:

[0052] Events (e.g., leaks, incomplete combustion) are logged with status (Normal / Abnormal / Critical), analyzed for root causes (e.g., via AI driven fault tree analysis), and assigned corrective suggestions. Alerts are customizable (e.g., text templates) and routed to recipients (e.g., groups via SMS / email / push notifications), integrable with internal systems like DCS / SCADA.

[0053] Solution 4: Enhancing Scalability, Integration, and User Collaboration for Holistic Flare Management

[0054] Overcoming the isolated nature of prior art systems, this invention includes scalable architecture for expanding sensors / flares, seamless integration with existing industrial systems, and a collaboration platform for knowledge sharing.

[0055] Components:

[0056] Scalable Architecture and Integration:

[0057] The system supports unlimited flares / cameras (default one flare) via modular addition (e.g., plug and play sensor nodes). Integration with DCS / SCADA uses APIs (e.g., OPC UA) for data transfer, enabling closed loop control (e.g., adjust steam / air inputs based on AI recommendations).

[0058] Incident Knowledge Exchange and Collaboration Platform:

[0059] A centralized web based platform for documenting incidents, lessons learned, and stakeholder collaboration (e.g., between operators and authorities like RIVM / ILT). Features include file storage (e.g., video uploads with processing for anomalies), searchable reports, and secure sharing tools.

[0060] Video Processing and Flare Stack Structural Monitoring:

[0061] Offline video uploads are processed using the same AI models for flame / smoke / gas analysis. Structural monitoring detects damage (e.g., corrosion via edge detection on optical images, accuracy >90%) and traffic control in flare areas.

[0062] Environmental Impact ROI Calculator:

[0063] This tool quantifies savings (e.g., emissions reduced in tCO₂e, cost savings from avoided shutdowns) using data from the digital twin, displayed on the dashboard for decision-making.

[0064] Comprehensive Dashboard and Indicators Module:

[0065] A unified interface with color coded statuses (e.g., green for normal), camera details, and indicators (e.g., carbon index alerts for breaches).

[0066] By addressing these technical challenges through an integrated platform of multi modal sensing, AI driven predictive digital twin, blockchain secured data, and automated compliance tools, the system establishes a new paradigm for flare management. This results in enhanced safety, reduced operational costs, verifiable regulatory compliance, and minimized environmental impact, providing a complete, reproducible solution for industrial applications.Advantage effects of invention

[0067] The present invention provides several advantageous effects over the prior art.

[0068] A primary advantage is the transition from a reactive to a proactive and predictive operational paradigm. Unlike prior art systems that detect failures as they occur, the disclosed invention utilizes a real time digital twin and an artificial intelligence engine to forecast potential failures, such as flare tip degradation, before they happen. This allows industrial facilities to perform scheduled maintenance instead of facing costly and dangerous unplanned shutdowns, significantly enhancing operational safety and resilience.

[0069] Another significant advantage is the provision of a trustworthy and auditable compliance mechanism. The integration of a blockchain based ledger ensures that all operational logs and compliance reports are immutable and tamper proof. This solves a critical problem of data integrity for regulatory bodies, providing a verifiable and secure record of compliance that is not offered by existing systems.

[0070] Furthermore, the invention yields the advantage of a drastic reduction in operational expenditure and manual labor. The automation of continuous monitoring eliminates the need for thousands of man hours spent on manual visual inspections, which are both costly and high risk. Similarly, the automated, one click generation of regulatory reports reduces the administrative burden on environmental teams from over 18 hours per month to a matter of minutes.

[0071] Finally, the invention results in the advantageous effect of improved environmental performance. By enabling the optimization of combustion processes based on real time data analysis, the system leads to a quantifiable reduction in harmful emissions, such as CO2, contributing to the sustainability goals of the facility and the wider industry.

[0072] : Proactive Predictive Flare Management Process.

[0073] : Data Integrity and Security Process with Blockchain.

[0074] : Automated Compliance Reporting Process.

[0075] : The following description, in conjunction with the accompanying drawing (), illustrates a preferred embodiment of the present invention. The invention provides an integrated, comprehensive system and method for proactive predictive flare management and automated, secure compliance reporting. The system is designed to transition flare operations from reactive monitoring to proactive control, enhancing safety, reducing operational costs, ensuring regulatory compliance, and minimizing environmental impact.

[0076] is a flowchart illustrating the core process of the proactive predictive flare management system according to one embodiment of the invention. The process begins with the flare system in operation (Start) and proceeds through several key stages including data acquisition, pre-processing, analysis, and proactive alerting.

[0077] As depicted in, the first major stage is Multi Modal Data Acquisition. In this stage, the system is configured to collect continuous, synchronized data streams from a plurality of diverse sensors. In one embodiment, these sensors include:

[0078] Optical Cameras: High resolution sensors, such as CMOS or CCD sensors with at least 1080p resolution and a 30 fps frame rate, are used to capture visible flame characteristics including height, volume, shape, and color.

[0079] Thermal Infrared (IR) Cameras: Dual band IR sensors, with spectral ranges of 3-5 μm and 8-14 μm and calibrated for temperatures up to 2000°C, are employed for thermography, heat mapping, and detecting flame detachment or pilot flame status.

[0080] Gas Composition Sensors: Non dispersive infrared (NDIR) analyzers are used to detect concentrations of gases such as CO2, CH4, NOx, and SO2 with an accuracy of at least ±1% full scale.

[0081] Physical Sensors: These include pressure transducers (e.g., piezoresistive sensors with a 0-100 psi range) and flow meters (e.g., ultrasonic or thermal mass flow meters) integrated into the flare header and stack.

[0082] Following data acquisition, the process moves to Data Pre processing. The sensor data is transmitted via industrial protocols, such as Modbus RTU or Ethernet / IP, to an on-premise edge computing unit (e.g., an industrial PC with a GPU like NVIDIA Jetson). This unit pre-processes the data, applying techniques such as Kalman filtering for noise reduction to enhance data quality.

[0083] The pre processed data is then fed into a Smart Digital Twin, which serves as the core processing engine. The digital twin is a real time virtual replica of the physical flare system, constructed using physics-based modeling. It incorporates historical baseline data (e.g., from an initial 30 day learning period) to model the normal operational states of the flare system.

[0084] The next stage is Data Analysis within the Digital Twin. An AI Engine analyzes deviations from the established baseline. In a preferred embodiment, the AI Engine utilizes deep learning models, including:

[0085] Convolutional Neural Networks (CNNs): Used for image analysis, performing tasks like semantic segmentation (e.g., via a U Net architecture) on optical and thermal images to detect flame and smoke with over 95% accuracy.

[0086] Recurrent Neural Networks (RNNs): Specifically, Long Short-Term Memory (LSTM) models, are used for time series forecasting based on data from physical and gas sensors.

[0087] Integrated within the digital twin is a Forecast Event Module. This module uses the AI analysis to predict the probability of operational failures. For instance, by correlating a subtle increase in flare-tip temperature with shifts in gas composition, it can use predictive models (e.g., Monte Carlo simulations) to project a future failure, such as a 75% risk of critical tip degradation within 14 days. This module is configured to achieve a low false-positive rate (e.g., <5%).

[0088] Based on the forecast, a Decision is made regarding a potential failure or abnormal event. If the probability of a failure exceeds a configurable threshold (Yes), the system proceeds to generate a Proactive Alert. The output is a notification sent via SMS, email, or a dashboard. These alerts include diagnostic information (e.g., thermal trend charts) and actionable corrective suggestions (e.g., "Schedule flare tip replacement"). If no failure is predicted (No), the system returns to the data analysis step, creating a continuous monitoring loop. The process concludes when the system is no longer in operation (End).

[0089] Whileillustrates the core predictive process, the invention also includes further integral components. In one embodiment, the system includes a Blockchain Powered Data Integrity Module (Solution 2). All operational data, including sensor readings and AI predictions, is cryptographically hashed (e.g., using SHA 256) and anchored to a permissioned blockchain ledger (e.g., Hyperledger Fabric). This creates a secure, tamper-proof, and immutable audit trail for all data, which is essential for regulatory compliance.

[0090] Furthermore, the system incorporates an Automated Compliance and Reporting Module (Solution 3). This module uses the blockchain verified data to automatically generate audit-ready reports tailored to specific regulatory formats (e.g., Dutch or EU standards) with a single click. This significantly reduces the administrative burden and ensures the trustworthiness of compliance data.

[0091] In another embodiment, the system is built on a Scalable Architecture (Solution 4), allowing for the modular addition of sensors and flares. It is designed for seamless integration with existing industrial control systems like DCS / SCADA via standard APIs (e.g., OPC UA), enabling closed-loop control where AI-driven recommendations can be automatically implemented.

[0092] : Turning now to, a detailed flowchart and component diagram of the Data Integrity and Security with Blockchain module is presented. This module is an integral component of the overall system, designed to address the technical problem of data verifiability and susceptibility to tampering found in prior art systems. It achieves this by creating a secure, tamper-proof, and verifiable ledger for all operational data generated by the system described in relation to.

[0093] As illustrated in, the module can be understood through its process flow, key components, and the resulting benefits.

[0094] Process Flow: The operational process for ensuring data integrity begins at Start with the generation of operational data. These Operational Data Sources include, but are not limited to, real time Sensor Readings from the multi-modal acquisition module, AI Predictions generated by the digital twin's AI engine, and Event Logs documenting system activities. The process then proceeds through the following steps:

[0095] Process: Hashing Data: The operational data is processed through a cryptographic algorithm. In a preferred embodiment, the SHA-256 algorithm is used to generate a unique and fixed-size hash for each data record.

[0096] Process: Attaching Timestamp & Digital Signature: Each transaction, such as the creation of a data hash, is cryptographically signed with a private key and timestamped. Access and signing privileges are managed via a role-based access control (RBAC) system.

[0097] Process: Anchoring Hash to Permissioned Blockchain: The generated hash, along with its associated metadata (e.g., event ID, timestamp), is anchored as a transaction on a permissioned blockchain ledger. This ledger may be based on frameworks such as Hyperledger Fabric or scalable, Ethereum-compatible chains like Polygon.

[0098] Parallel Process: Storing Raw Data in Encrypted Database: Concurrently, the raw operational data itself is stored off-chain in a secure, encrypted database. In one embodiment, this database utilizes AES 256 encryption and complies with data protection regulations such as GDPR. This hybrid approach ensures both performance and immutability.

[0099] Output: The culmination of this process is the Creation of an Immutable & Verifiable Audit Trail. The process concludes at End for each data anchoring cycle.

[0100] Key Components: The functionality of this module is enabled by a set of Key Components as shown in:

[0101] Cryptographic Algorithm: A standard, secure hashing function like SHA-256 is used to ensure that any change to the original data results in a completely different hash.

[0102] Timestamp and Digital Signature: These components provide non repudiation and temporal proof, verifying when a record was created and by whom.

[0103] Permissioned Blockchain Ledger: A distributed ledger accessible only to authorized participants (e.g., operators, auditors, regulators). Its consensus mechanism ensures that once a record is anchored, it cannot be altered or deleted.

[0104] Secure, Encrypted Database: An off-chain storage solution for the raw data, protecting sensitive information while ensuring it is linked to the immutable proof on the blockchain via its hash.

[0105] Benefits and Output: The primary output and technical benefit of this module is a trustworthy data record system for compliance and auditing purposes.

[0106] Immutable Audit Trail: Once data is hashed and anchored to the blockchain, it becomes part of a permanent, unchangeable record. This provides an unprecedented level of trust in the data.

[0107] Verifiable Audit Trail: The integrity of the data can be independently verified by external parties, such as Regulatory Authorities. In a specific embodiment, this verification can be performed by authorities like the Dutch RIVM or ILT. This can be accomplished using advanced cryptographic methods like zero knowledge proofs (zk SNARKs), which allow for the confirmation of data validity without exposing the sensitive underlying data itself. This ensures both transparency and confidentiality.

[0108] :presents a detailed flowchart of the Automated Compliance Reporting Workflow, which constitutes a further critical aspect of the invention. This module addresses the technical problem of inefficiencies, high administrative burden, and fragmentation associated with manual compliance reporting in prior art systems. By automating the process, the invention reduces report preparation time from hours (e.g., 18 hours) to minutes (e.g., under 10 minutes) while ensuring data verifiability.

[0109] The workflow initiates at the Start block. The initiation can be triggered in one of two primary ways:

[0110] User Initiation: An authorized user can trigger the process via a "One Click Report Generation" feature, typically available on the system's dashboard.

[0111] System Initiation: The system can be configured to automatically generate reports on a pre defined schedule (e.g., daily, weekly, or monthly), resulting in a Scheduled Report.

[0112] Upon initiation, the workflow enters the main Process stage, which comprises several sequential steps:

[0113] Data Access: The Compliance Module first accesses the necessary data. This includes both Real Time Data streamed from the digital twin (described in) and Historical Stored Data retrieved from the system's secure database.

[0114] Data Comparison: The module then performs a Data Comparison, wherein the operational data is cross-referenced against pre-configured Regulatory Thresholds. In a preferred embodiment, these thresholds are based on relevant regulations such as Dutch Standards (e.g., Dutch Environmental Management Act) and broader EU Standards (e.g., IED, EU ETS), including specific limits like emission thresholds for NOx.

[0115] Violation Detection Decision: Based on the comparison, a decision is made. If a violation is detected (Yes), the system is configured to immediately Trigger an Immediate Alert for Operators. If all data points are within compliant limits (No), the system will Continue Process.

[0116] Compile Data: The compliant data is then compiled into a report using Pre defined Templates. These templates are tailored to meet the specific formatting requirements of regulatory bodies, such as the NEa Format or ILT Format used by Dutch authorities.

[0117] Add Visual Analytics: To improve readability and analytical insight, the system automatically adds Visual Analytics to the report. This can include Charts, such as a line chart showing flame height over time, and Thermal Heat Maps generated from the thermal camera data.

[0118] Retrieve Proof of Data Integrity: In a crucial step that integrates the different modules of the invention, the system interfaces with the blockchain module (described in) to Retrieve the Proof of Data Integrity corresponding to the data in the report. This cryptographic proof is then programmatically Attached to the Report.

[0119] Finally, the workflow moves to the Output stage. The system Generates the Final, Audit Ready Report, which now contains not only the operational data and analytics but also the verifiable proof of its integrity. The report can be exported in multiple formats as needed, such as a PDF Format for official submission or an Excel Format for further analysis. The process then concludes at the End block.Examples

[0120] EXAMPLE 1: Predictive Detection of Flare Tip Degradation and Automated Compliance Reporting

[0121] This example illustrates a mode of operation for the inventive system in a typical petrochemical facility.

[0122] System Configuration and Data Ingestion: A flare stack at the facility is instrumented with the system. This includes two multi spectral cameras (one optical, one thermal) positioned to have a clear field of view of the flare tip and flame. Additionally, physical sensors are integrated, including gas composition sensors for CO2, CH4, and (NO(x)) at the stack outlet, and pressure monitors within the flare header. All data streams are transmitted to an on premise processing unit.

[0123] Normal Operation and Digital Twin Baselining: For the first 30 days of operation, the system operates in a learning mode. It collects continuous data on flame characteristics (height, temperature, color), gas composition, and pressure under various normal operating loads. The artificial intelligence engine uses this data to construct a dynamic, high-fidelity

[0124] digital twin of the flare system, establishing a healthy operational baseline that accounts for normal fluctuations.

[0125] Predictive Anomaly Detection via the Digital Twin: On day 45, the system's AI engine, by analyzing the live data against the digital twin's baseline, detects a subtle but persistent thermal anomaly. The thermal camera data shows a minute, localized increase in the flare tip's surface temperature that is outside the normal operational signature. The system correlates this with a minor (0.5%) decrease in combustion efficiency, detected by the gas composition sensors. The AI engine, using its predictive models on the digital twin, forecasts that this trend indicates early stage material degradation of the flare tip and projects a

[0126] 75% probability of critical failure within the next 14 days.

[0127] Smart Alerting and Action: Immediately upon this prediction, the system generates a Medium Priority alert and routes it to the dashboards of the maintenance team. The alert is not just a simple warning; it includes a full diagnostic report with the thermal trend data, the combustion efficiency graph, the predicted failure window, and a corrective suggestion to schedule an inspection and potential replacement of the flare tip during the next planned maintenance cycle. This allows the facility to avoid a costly and dangerous unplanned shutdown.

[0128] Automated Reporting and Blockchain Anchoring: Following a successful scheduled maintenance on day 52, a maintenance log is entered into the system. The system automatically compiles a comprehensive Proactive Maintenance & Compliance Report. This report includes all data related to the incident: the initial predictive alerts, the supporting thermal and gas data, the maintenance log, and confirmation of restored normal operation. A cryptographic hash of this final, verified report is generated and anchored as a permanent, timestamped record on the system's

[0129] blockchain ledger. This immutable record serves as verifiable proof of proactive compliance and due diligence, ready for any future audit by regulatory bodies like the ILT.

[0130] The disclosed invention, comprising a system and method for predictive management and automated compliance of industrial flares, has direct and substantial applicability in various industrial sectors. The system is capable of being manufactured and implemented in industries that utilize flare stacks for the management of waste gases.

[0131] Specifically, the invention is applicable to:

[0132] The oil and gas industry, including upstream (extraction), midstream (transport and storage), and downstream (refining) operations.

[0133] The petrochemical industry, for monitoring flares in chemical production plants.

[0134] Power generation plants that use flaring systems during startup, shutdown, or emergency procedures.

[0135] The invention provides a tangible solution for enhancing operational safety, optimizing maintenance schedules through predictive analytics, reducing costly unplanned shutdowns, and ensuring compliance with stringent environmental regulations within these industries. Therefore, the invention is not merely a theoretical concept but has clear industrial utility and can be readily applied in a commercial and industrial context.

Claims

A predictive flare management system comprising:a multi modal data acquisition module including at least one optical camera, at least one thermal camera and one or more physical sensors selected from gas composition sensors and pressure sensors, each arranged to capture data related to flare operation;a processing circuitry configured to:(a) receive the multi-modal data and generate a real time dynamic digital twin of the flare system that models current operational parameters;(b) execute an artificial intelligence (AI) engine that compares live data to the digital twin baseline and performs predictive analysis to forecast potential operational failures including material degradation, structural defects and combustion inefficiencies;(c) generate smart alerts when the predictive analysis indicates a probability of failure beyond a threshold; and(d) create a secure compliance report containing operational data and analysis, wherein a cryptographic hash of the report is anchored to a tamper proof blockchain ledger to provide immutable proof of data integrity, wherein the blockchain ledger is configured to allow verification of the report's integrity through privacy-preserving cryptographic proofs, including zero-knowledge proofs (zk-SNARKs), enabling a regulatory authority to verify data integrity without accessing the raw operational data.(e) Automated generation of a secure compliance report in a format suitable for a regulatory body, based on a predefined schedule or a user initiated command.a user interface configured to display alerts and reports to authorized operators and to provide one click initiation of regulatory reports.The system of claim 1 wherein the physical sensors comprise gas composition sensors for carbon dioxide (CO2), methane (CH4) and nitrogen oxides (NO(x)) mounted at a flare stack outlet and pressure sensors mounted in a flare header to monitor process pressure.The system of claim 1 wherein the optical camera comprises a multi spectral or hyper spectral camera arranged to capture flame characteristics such as height, temperature and color; and the thermal camera is arranged to capture infrared temperature distributions of the flare tip.The system of claim 1 wherein the processing circuitry is configured to continuously update the digital twin using incoming data and to train the AI engine during an initial learning period to establish a baseline representing normal operating behaviour.The system of claim 1 wherein the AI engine employs machine learning algorithms to detect deviations from the baseline and to compute a probability of critical failure within a forecast window; the system triggers alerts when the probability exceeds a predetermined threshold.The system of claim 1 wherein the smart alert includes diagnostic information comprising at least one of: trend graphs of thermal data, combustion efficiency graphs derived from gas composition, a predicted failure window, and recommended maintenance actions.The system of claim 1 further comprising a compliance module configured to:retrieve real time and historical operational data from the digital twin and a secure database;compare the data to regulatory thresholds stored in memory;generate an alert if any parameter exceeds the thresholds;compile compliant data into a report using predefined templates tailored to regulatory bodies;automatically add visual analytics to the report, including charts and thermal heat maps; andattach the blockchain proof of data integrity to the report before export.The system of claim 7 wherein the regulatory thresholds comprise emission limits defined by national or regional regulations, and the report is exportable in at least PDF and spreadsheet formats.The system of claim 1 wherein the blockchain ledger stores hashes of reports and allows verification of report integrity through cryptographic proofs, said proofs including zero-knowledge proofs (zk-SNARKs) configured to allow a regulatory authority to verify data integrity without accessing the raw operational data.The system of claim 1 wherein raw multi modal data are stored in an encrypted off chain database linked to the blockchain by their hashes, thereby preserving data confidentiality while enabling auditability.The system of claim 1 wherein the processing circuitry is configured to communicate predictive alerts and compliance information to external government regulatory systems via a secure interface.A method for predictive management and automated compliance of a flare system comprising the steps of:(a) continuously acquiring data from optical cameras, thermal cameras and physical sensors associated with a flare stack;(b) constructing and updating a real time digital twin of the flare system from the data;(c) performing predictive analysis on the digital twin via an AI engine to forecast potential operational failures;(d) issuing smart alerts when the predicted probability of failure exceeds a threshold;(e) comparing real time and historical data to pre configured regulatory thresholds and generating compliance alerts if violations are detected;(f) compiling compliant data into a report, adding visual analytics, and anchoring a cryptographic hash of the report onto a blockchain ledger; and(g) Outputting reports for use by operators or for submission to a regulatory body.The method of claim 12 wherein step (b) includes training the AI engine during a baseline period to establish normal operating ranges for parameters such as flame height, temperature, color, gas composition and pressure.The method of claim 12 wherein step (c) includes correlating thermal anomalies detected by the thermal camera with changes in combustion efficiency derived from gas composition sensors to predict flare tip degradation and scheduling maintenance prior to failure.The method of claim 12 wherein step (e) comprises referencing emission thresholds defined by European Union Emissions Trading System (EU ETS) or other national regulations and generating immediate alerts to operators when the thresholds are exceeded, and step (f) comprises adding charts and heat maps to enhance readability of the report.

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