Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure
Bibliographic record
Abstract
Industry 5.0 raises awareness towards converting conventional industrial technologies into smart technologies integrated with sustainable infrastructure for efficiently handling process systems, making them more energy and cost-efficient. New disruptive technologies are emerging due to recent scientific and technical developments, which profoundly affect various process systems. One such case of consideration is the algal cultivation for biomass production (ACB). A technology called an algal digital twin (ADT) has a great deal of promise to change existing ACB (For example raceway pond) into sustainable algal management systems (Nitrogen, Phosphorus, Temperature, Turbidity, Dissolved Oxygen (DO), Carbon dioxide (CO 2 ), pH, Chlorophyll-a, etc.), and to develop their infrastructure in making them more energy efficient and cost-effective for the algal biomass cultivation. However, despite a recent increase in attention, there have not been adequate investigations exploring the challenges of deploying ADTs for controlling and monitoring ACB. This review provides a systematic literature analysis on adopting an ADT into ACB, which could address major difficulties and unresolved problems of the ACB. Also, this study identifies several key categories of hurdles, such as interconnection and semantics, facilities, acquiring data and actuation, data reliability, modelling (Artificial Intelligence of Things), simulation run, decision making, digitalization of data, accountability, as well as social concerns. Additionally, case studies for the ACB towards lipid production and wastewater treatment using ADT are reported. Overall, this comprehensive review aims to help practitioners gain insight into the deployment of ADT into ACB systems, “A way towards creating a sustainable smart infrastructure for ACB”. • Provide a comprehensive overview of DT modelling from the perspectives of ACB • Bridging gaps and unlocking potential for ADT in smart algal management • Integration of ADT in ACB enhances efficiency and resource management • Case studies for ACB towards lipid production and wastewater treatment using ADT • Potential research directions for the ADT model and recommendations for ACB
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".