Optimal and sustainable design of integrated biorefineries for microalgae and municipal solid waste processing
Bibliographic record
Abstract
Increasing pressures on energy resources and the imperative to reduce greenhouse gas emissions are driving the exploration of alternative and viable energy sources. Biomass presents opportunities to produce a variety of valuable products including energy, chemicals, and materials. However, economic uncertainties arise from processing multiple biomass sources, and to address this issue, the current research covers a systematic framework for the optimal design of an integrated biorefinery through superstructure-based optimization, combining microalgae and municipal solid waste (MSW) processing pathways. A case study conducted for Seoul, South Korea—a city grappling with significant solid waste management and energy supply challenges—evaluates the economic feasibility of such an integrated biorefinery. Utilizing mixed-integer linear programming (MILP) in General Algebraic Modeling System (GAMS), the study identifies the optimal configuration for the biorefinery to maximize profitability. Results indicate that the proposed solution not only yields substantial quantities of valuable products but also reduces waste sent to landfills and enhances waste-to-electricity conversion. The end-products of the optimal configuration include all anticipated products except compost. The revenue from the sale of final products and the profit are $1.43 B/yr and $253.86 M/yr respectively for the optimal configuration obtained. Additionally, a sensitivity analysis assessing the impact of varying economic and feedstock conditions on the biorefinery's profitability and viability shows that the proportion of recyclable components in MSW has the biggest impact, followed by the market price of the biodiesel produced. The framework is generic as the superstructure can be modified in line with the requirements of the case at hand by selecting the appropriate feedstocks and technologies. Moreover, the relevant techno-economic parameters and equations can easily be incorporated into the mathematical model. Thus, this study has the potential to serve as a valuable decision-making tool for stakeholders when planning viable multi-feedstock biorefineries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".