Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm
Bibliographic record
Abstract
The alumina evaporation process (AEP) is an indispensable step for the reuse of sodium aluminate solution by evaporating excess water contained in the solution. The selection of optimal operating parameters is a complicated task because the process is influenced by many nonlinear factors when both the quality and quantity of the product are concerned. In this paper, we formulate a multi‐objective optimization model to maintain the balance of operating costs and energy efficiency in AEP, and a multi‐objective state transition algorithm (MOSTA) is proposed for solving this problem. With the aim of solving the constrained multi‐objective problem, a search archive strategy of elite populations and a novel infeasible solution replacement mechanism are integrated into STA. Some infeasible solutions with better performances are allowed to be saved and participate randomly in the evolution to select optimal solutions from all possible directions. A mutation operator is introduced into the evolutionary process to enhance the global search ability. Simulation results from some benchmark test problems show that the proposed method tends to converge quickly and effectively to the true Pareto frontier with better distribution. The proposed algorithm is successfully applied to solve the multi‐objective optimization problem arising in AEP. The optimal results show that operating costs and energy loss are considerably reduced, by approximately 13.63 % and 13.39 %, respectively.
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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 | 0.001 | 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".