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Titlebook: BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems; Urmila Diwekar,Amy David Book 2015 Urmila Diwekar, Amy David 20

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樓主: Forestall
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發(fā)表于 2025-3-26 21:29:16 | 只看該作者
32#
發(fā)表于 2025-3-27 05:03:19 | 只看該作者
33#
發(fā)表于 2025-3-27 07:21:55 | 只看該作者
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發(fā)表于 2025-3-27 10:52:30 | 只看該作者
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發(fā)表于 2025-3-27 13:59:44 | 只看該作者
Sensor Placement Under Uncertainty for Power Plants, then applied to the problem of sensor placement in an integrated gasification combined cycle power plant, and how BONUS significantly reduces computational resources while contributing to an appropriate solution is shown. This chapter is derived from the work by Lee and Diwekar.
36#
發(fā)表于 2025-3-27 17:55:56 | 只看該作者
https://doi.org/10.1007/978-1-4939-2282-6BONUS algorithm; SNLP; Stochastic Programming; power systems; sensor placement; water management
37#
發(fā)表于 2025-3-27 23:05:09 | 只看該作者
38#
發(fā)表于 2025-3-28 03:32:14 | 只看該作者
39#
發(fā)表于 2025-3-28 07:53:15 | 只看該作者
Nutrition and Diabetic Retinopathy,thermoelectric power generation increases in the USA (the Energy Information Administration estimates that thermoelectric power generation will grow 22 % by 2030), so does the importance of managing the water used in these plants. In a clean coal-fired power plant, water is consumed in makeups (wate
40#
發(fā)表于 2025-3-28 12:22:20 | 只看該作者
Michelle Price Judge,Cheryl Tatano Beckanage due to its dependence on ambient weather conditions. Because weather conditions are both constantly changing and uncertain, a stochastic framework for real-time optimization of power generation is advantageous over a deterministic framework in maximizing power output. In this chapter, it is sh
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