This work presents a decision-making framework for global optimization of detailed renewable energy processes considering technological uncertainty. The critical uncertain sources are identified with an efficient computational method for global sensitivity analysis, and are obtained in two different ways, simultaneously and independently per product pathway respect to the objective function. For global optimization, the parallel stochastic response surface method developed by Regis & Shoemaker (2009) is employed. This algorithm is based on the multi-start local metric stochastic response surface method explored by the same authors (2007a). The aforementioned algorithm uses as response surface model a radial basis function (RBF) for approximating the expensive simulation model. Once the RBFs parameters are fitted, the algorithm selects multiple points to be evaluated simultaneously. The next point(s) to be evaluated in the expensive simulation are obtained based on their probability to attain a better result for the objective function. This approach represents a simplified oriented search. To evaluate the efficacy of this novel decision-making framework, a hypothetical multiproduct lignocellulosic biorefinery is globally optimized on its operational level. The obtained optimal points are compared with traditional optimization methods, e.g. Monte-Carlo simulation, and are evaluated for both proposed types of uncertainty calculated.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-04052016-185104 |
| Date | 19 April 2016 |
| Creators | Salas Ortiz, Santiago David |
| Contributors | Flake, John, Hung, Francisco, Liao, Warren, Romagnoli, Jose |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-04052016-185104/ |
| Rights | restricted, I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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