Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it