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Data Collection Guidelines

This document outlines general principles, sources, procedures and quality control requirements for data collection.

Data Collection Process​

Data Sources​

Data can be collected from the following types of public literature and materials:

  • Research Papers: Journal articles, conference papers (recommended platforms: Elsevier, Springer, ACS, CNKI, Google Scholar)
  • Engineering Manuals: Such as "Chemical Engineering Design Manual", "Chemical Process Manuals"
  • Corporate Environmental Impact Reports: Including EIA reports, feasibility studies, cleaner production audit reports
  • Government & Industry Publications: Statistical yearbooks, industry standards, energy statistics, industry development bluebooks
  • Patents & Standards: National/industry standards, patent databases (e.g., CNIPA, WIPO)
  • Field Survey Data: Actual measurement data from factories, enterprises or research institutions

Data collection must follow two fundamental principles:

  • Each data point must be fully traceable, with source clearly documented (literature/report/database)
  • All entered information should be complete and detailed to ensure data quality and reproducibility

Process Flow Analysis​

Before data collection begins, identify the unit process-level industry flow diagram.

Literature Search & Selection​

Use keywords to search data on platforms like CNKI, WoS, Scopus and Google Scholar. Recommended keyword combinations:

  • "Methanol life cycle assessment"
  • "Inventory analysis"
  • "Environmental impact" etc.

Prioritize original or high-quality studies with clear input-output data and system boundaries.

Collection Content​

Main data categories to collect:

  • Material inputs (raw materials, water, additives)
  • Energy consumption (electricity, fuels)
  • Product & byproduct outputs (methanol, byproduct gases, waste heat)
  • Emission data (CO₂, NOₓ, wastewater, solid waste)

Data Processing​

Unit Standardization & Conversion​

Convert units from different sources to standard units to ensure consistency and reusability in the database. Examples:

  • Energy conversion: kcal → MJ
  • Volume-mass conversion: Nm³ → kg (based on density or standard conditions)

Important Note: When required units differ from existing flow units, perform unit conversion rather than creating new Flow items.

Missing Value Handling​

Available strategies:

  • Average values from similar processes
  • Typical values from LCA databases
  • Engineering estimates (e.g., heat balance, material balance)
  • Clearly document assumptions or uncertainty ranges

Special Notes & Remarks​

For lifecycle modeling, document the following in remark fields to ensure standardized dataset structure, clear system boundaries and transparent data processing:

  • UUID Conflict Notes: Input/output should not contain duplicate flow items (platform distinguishes by UUID). If literature shows same substance in different forms/phases, merge into single flow item and document rationale.

  • Internal Recycling Notes: For flows like steam that are internally recycled without additional environmental burden, note: "Internal platform recycling flow, not counted as output emissions".

  • Excluded Emission Notes: For items like catalyst loss that involve material flow but are contained within system, note: "Catalyst operates in closed loop, not included in material balance output".

  • Estimation/Completion Notes: For items completed based on material balance or engineering experience (e.g., wastewater, exhaust), note: "Estimated based on material balance, original data not provided in literature".