Multimodel Operability Framework for Design of Modular and Intensified Energy Systems
Bibliographic record
Abstract
In this dissertation, a novel operability framework is introduced for the process design of modular and intensified energy systems that are challenged by complexity and highly constrained environments. Previously developed process operability approaches are reviewed and further developed in terms of theory, application, and software infrastructure. An optimization-based multilayer operability framework is introduced for process design of nonlinear energy systems. In the first layer of this framework, a mixed-integer linear programming (MILP)-based iterative algorithm considers the minimization of footprint and achievement of process intensification targets. Then, in the second layer, an operability analysis is performed to incorporate key features of optimality and feasibility accounting for the system achievability and flexibility. The outcome of this framework consists of a set of modular designs, considering both the aspects of size and process operability. For this study and throughout this dissertation, the nonlinear system is represented by multiple linearized models, which results in lower computational expense and more efficient quantification of operability regions. A systematic techno-economic analysis framework is also proposed for costing intensified modular systems. Conventional costing techniques are extended to allow estimation of capital and operating costs of modular units. Economy of learning concepts are included to consider the effect of experience curves on purchase costs. Profitability measures are scaled with respect to production of a chemical of interest for comparison with plants of traditional scale. Scenarios in which the modular technology presents break-even or further reduction in cost when compared to the traditional process are identified as a result. A framework for the development of process operability algorithms is provided as a software infrastructure outcome. Generated codes from the developed approaches are included in an open-source platform that will give researchers from academia and industry access to the algorithms. This platform has the purpose of dissemination and future improvement of process operability algorithms and methods. To show versatility and efficacy of the developed approaches, a variety of applications are considered as follows: a membrane reactor for direct methane aromatization conversion to hydrogen and benzene (DMA-MR), the classical shower problem in process operability, a power plant cycling application for power generation with penetration of renewable energy sources, and a newly developed modular hydrogen unit. Applications to DMA-MR subsystems demonstrate employment of the multilayer framework to find a region with modular design candidates, which are then ranked according to an operability index. The most operable design is determined and contrasted with the
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".