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Record W1972953677 · doi:10.1021/ie060652h

Optimization of Aluminum Smelter Casthouse Operations

2006· article· en· W1972953677 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2006
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsHoneywell (Canada)
Fundersnot available
KeywordsMathematical optimizationComputer scienceA priori and a posterioriScheduling (production processes)Integer programmingAlgorithmIterative and incremental developmentSet (abstract data type)Variable (mathematics)Process (computing)Material balanceLinear programmingProcess engineeringMathematicsEngineering

Abstract

fetched live from OpenAlex

This paper presents a mixed-integer linear programming (MILP) model for the scheduling of a multistage process for the production of aluminum casts of different alloys, using parallel furnaces and casters. In contrast to the common approach in multistage models of considering a fixed set of orders being processed sequentially in the stages, the modeling approach in this paper accounts for actual material flows and, thus, provides flexibility with respect to the actual number of batches to be processed for meeting a given demand. This enables the model to handle parallel nonuniform units with variable capacities, where it is difficult to a priori decide on the number of batches that are required to satisfy the orders. The model also features a material balance over the furnace section, to capture processing details. A decomposition scheme that consists of a master problem and a sub-problem is developed and is used in an iterative algorithm to solve medium- to large-sized problems in reasonable computational time.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.291
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it