Fuel Oil Monitor

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System Architecture & Data Flow

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Laboratory Integration Network

Our module integrates with six leading marine fuel testing laboratories through different data channels:

Laboratory	Integration Method	Data Format	Processing Type
Viswa Labs	API Integration	JSON	Real-time API calls
Tribocare	API Integration	JSON	Real-time API calls
FOBAS	API Integration	JSON	Real-time API calls
VPS Marine	Snowflake Database	Structured Data	Database queries
Bureau Veritas (BV)	Email Attachments	XML + Report	XML file extraction
Maritec	Email Attachments	XML + Report	XML file extraction

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Process Workflow: Step-by-Step Implementation

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Phase 1: Data Acquisition & Integration

Objective: Establish comprehensive fuel oil data collection from multiple laboratory sources using different integration methods

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Step 1: API-Based Data Collection

The Challenge: Traditional batch processing approaches failed to handle the dynamic nature of laboratory data updates, leading to data inconsistencies and processing gaps. Our Revolutionary Solution: We engineered an intelligent cyclic data synchronization system that maintains perfect data integrity across all API-based laboratories. Key Innovation: This cyclic approach ensures zero data loss while optimizing API calls and maintaining real-time synchronization across all laboratory sources.

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Step 2: Snowflake Database Integration

Implementation: VPS Marine Data Extraction Snowflake Integration Features:

• Direct Database Connection: Secure connection to VPS Snowflake instance
• Scheduled Queries: Automated data retrieval at regular intervals
• Data Warehouse Access: Access to historical and real-time VPS fuel analysis data
• Query Optimization: Efficient data extraction with minimal resource usage

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Step 3: Email-Based Data Processing with XML Extraction

Implementation: BV and Maritec Laboratory Data Email Processing Workflow:

• Email Monitoring: Continuous monitoring of designated email accounts
• Attachment Identification: Automatic detection of emails with attachments
• Dual File Processing:
  • XML File: Contains structured data for extraction
  • Report File: Actual laboratory report for reference
• XML Data Extraction: Automated parsing of XML files to extract fuel analysis data
• Data Validation: Verification of extracted data against laboratory standards

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Phase 2: Data Processing & Standardization

Objective: Process and standardize data from multiple sources into unified format

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Step 4: Multi-Source Data Processing

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Step 5: Data Validation & Quality Assurance

Validation Framework:

• Schema Validation: Ensuring data conforms to expected structure
• Data Type Verification: Confirming correct data types for all fields
• Range Checking: Validating fuel parameters are within acceptable ranges
• Duplicate Detection: Identifying and handling duplicate test results
• Missing Data Handling: Managing incomplete or missing data points

Fuel Classification Algorithm: Advanced fuel classification system that handles all edge cases and data variations with mathematical precision: $\text{Fuel Classification Algorithm} = f(\text{BDN Sulfur}, \text{Lab Sulfur})$ Classification Logic: $$\text{Fuel Type} = \begin{cases} \text{ULSFO/LSMGO} & \text{if } S_{\text{effective}} \leq 0.1\% \\ \text{VLSFO} & \text{if } 0.1\% < S_{\text{effective}} \leq 0.5\% \\ \text{HSFO} & \text{if } 0.5\% < S_{\text{effective}} < 3.6\% \\ \text{Cannot\ be\ calculated} & \text{if } S_{\text{effective}} = \varnothing \\ \text{Unknown} & \text{if } S_{\text{effective}} \geq 3.6\% \end{cases}$$ Where: $$S_{\text{effective}} = \begin{cases} \text{BDN Sulfur} & \text{if available} \\ \text{Lab Sulfur} & \text{otherwise} \end{cases}$$

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Step 6: Data Transformation & Standardization

Transformation Process:

• Unit Standardization: Converting all measurements to standard units
• Field Mapping: Mapping laboratory-specific fields to unified schema
• Data Enrichment: Adding metadata and processing timestamps

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Phase 3: Database Storage & Management

Objective: Efficiently store processed data in MongoDB with proper indexing and organization

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Step 7: MongoDB Storage Architecture

Storage Features:

• Indexing Strategy: Optimized indexes for fast query performance
• Data Partitioning: Efficient data organization by vessel and date
• Backup & Recovery: Automated backup and disaster recovery procedures

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Step 8: Data Repository Management

Repository Features:

• Version Control: Tracking data changes and updates
• Audit Trail: Complete logging of all data processing activities
• Data Lineage: Tracing data from source to final storage

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Phase 4: Advanced Analytics & Risk Assessment

Objective: Implement sophisticated fuel analysis algorithms for risk assessment and compliance monitoring

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Step 9: CatFine (Aluminium + Silicon) Risk Categorization System

Implementation Strategy: Our advanced CatFine analysis system extracts concentration data from the latest bunkering operations and applies multi-tier risk assessment protocols. Primary Safety Threshold: A critical threshold of 15 mg/kg is implemented as the primary safety benchmark: $$\text{Primary Risk Assessment} = \begin{cases} \text{Safe Level} & \text{if } \text{CatFine} \leq 15 \ \text{mg/kg} \\ \text{Risky Level} & \text{if } \text{CatFine} > 15 \ \text{mg/kg} \end{cases}$$ Enhanced Multi-Tier Risk Classification: Our system implements three sophisticated risk bands for comprehensive vessel safety management: $$\text{Advanced Risk Categorization} = \begin{cases} \text{Safe Level} & \text{if } \text{CatFine} \leq 15 \ \text{mg/kg} \\ \text{Moderately Elevated} & \text{if } 15 < \text{CatFine} \leq 25 \ \text{mg/kg} \\ \text{Elevated Risk} & \text{if } 25 < \text{CatFine} \leq 35 \ \text{mg/kg} \\ \text{Dangerously High} & \text{if } \text{CatFine} > 35 \ \text{mg/kg} \end{cases}$$ Risk Level Descriptions:

• Safe Level (≤ 15 mg/kg): Vessel operates within optimal safety parameters
• Moderately Elevated (15-25 mg/kg): Minimal risk profile with recommended monitoring protocols
• Elevated Risk (25-35 mg/kg): Requires close monitoring and enhanced fuel treatment procedures
• Dangerously High (> 35 mg/kg): Critical status requiring immediate intervention and emergency protocols

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Step 10: Sulfur Compliance Verification System

Compliance Algorithm: Our intelligent compliance system performs real-time comparison between laboratory-tested sulfur values and Bunker Delivery Note (BDN) specifications for each vessel in the fleet. $$\text{Sulfur Compliance Status} = \begin{cases} \text{Compliant} & \text{if } S_{\text{tested}} \leq S_{\text{BDN}} \\ \text{Non-Compliant} & \text{if } S_{\text{tested}} > S_{\text{BDN}} \\ \text{Not Applicable} & \text{if } S_{\text{tested}} = \varnothing \ \text{or } S_{\text{BDN}} = \varnothing \end{cases}$$ Fleet-Level Compliance Assessment: The system generates comprehensive fleet-wide compliance reports:

• Individual Vessel Analysis: Detailed compliance status for each vessel
• Non-Compliant Vessel Identification: Automatic flagging and listing of vessels exceeding BDN limits
• Fleet Compliance Summary: Overall fleet status with full compliance verification

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Step 11: Advanced Visualization & Interactive Analytics

Dual-Plot Visualization System: Our module generates two sophisticated interactive plots using Plotly for comprehensive fuel analysis visualization: CatFine Risk Visualization: Sulfur Compliance Visualization: Visualization Features:

• Interactive Interface: Real-time data exploration with zoom and filter capabilities
• Multi-Parameter Display: Simultaneous visualization of BDN and tested values
• Compliance Indicators: Visual compliance status with color-coded vertical lines
• Threshold Markers: Clear safety threshold indicators for immediate risk assessment