Integration of LCA, TEA, Process Simulation and Optimization: A systematic review of current practices and scope to propose a framework for pulse processing pathways
Bibliographic record
Abstract
It is now common practice to conduct either a life cycle assessment (LCA) or techno-economic analysis (TEA) to assess the feasibility and sustainability profiles of specific technologies or product supply chains. Although numerous studies have proposed integrated frameworks for combining LCA and TEA for specific sectors, such a framework has not been proposed for the pulse protein processing sector to date. The goal of the current analysis was to propose such a framework including, in addition, integration of process simulation and optimization capabilities, that can enable assessing and improving the sustainability of existing and emerging pulse protein extraction pathways (i.e., dry fractionation, wet fractionation, hybrid) based on a combination of technical, economic, and environmental performance criteria. A systematic review of published articles was used to identify the key characteristics of sector-specific integrated frameworks and to subsequently propose a comparable framework for pulse processing pathways, taking into consideration relevant attributes of LCA and TEA studies of agri-food processing systems. Different system boundaries and functional units are commonly utilized for LCA (cradle to gate) and TEA/process simulation (gate to gate), but the proposed framework proposes using the same functional units (both mass and functionality based) based on output material. In addition to adhering to the ISO 14044 standard for LCA and established TEA methodologies, the proposed framework recommends integrating process simulation, genetic algorithm-based multi-objective optimization, GIS models for spatially explicit raw material production scenarios, and use of analytical hierarchy process to facilitate multi-criteria decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".