BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems

David, Amy; Diwekar, Urmila M.

doi:10.1007/978-1-4939-2282-6

Cited by 21 publications

(21 citation statements)

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“…It is to be mentioned that any monthly demand and resource variation other than the one used in the present model obtained from past data could have been used for the analysis. The kernel density estimator (KDE), as described below, is used for probability estimation (Diwekar and David 2015). Water demands are found to vary throughout the year for domestic (domdem r ), industrial (inddem r ) as well as agricultural (abrdem r ) usages.…”

Section: Uncertainty In the Water Network Modelmentioning

confidence: 99%

“…Uncertainty at different scenarios will impact the sale price and other variable costs including pumping and operating costs and the probability of a scenario depends on those variables. The probability density are obtained using kernel density estimator (KDE) (Diwekar and David 2015). In this work, past data have been used to create probability of uncertain variables in the future with the assumption that in future years the demand and resource uncertainty is known.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Design of Water Distribution Networks with Incorporation of Uncertainties and Energy Nexus

Mukherjee¹,

González-Bravo

Nápoles‐Rivera

et al. 2017

Process Integr Optim Sustain

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The reliability of water distribution networks is susceptible to large impacts resulting from uncertainties. Several factors may contribute to such uncertainties. These include nodal demands, reservoir/tank levels, and availability of system components. Additional sources of uncertainties arise when water-energy nexus is considered. For instance, when water desalination systems that are coupled with power plants are incorporated in the water distribution networks, uncertainties in energy demand influence the design of the water network system. In this work, a multi-objective optimization framework is developed for the design of water networks that involve dual-purpose power plants for simultaneous water desalination and power generation. The multi-objective model accounts for sustainability goals through environmental, economic, and societal metrics such as greenhouse gas emissions, net profit, and number of jobs created, respectively. Fossil fuels, as well as renewable biofuels and solar power, are considered as candidate sources of energy. Water resources include multiple systems such as dams, lakes, rivers, aquifers, and artificial storage lakes. Demands from agricultural, industrial as well as domestic needs in the region are considered. In order to incorporate uncertainty in the proposed approach, a stochastic optimization model is formulated with a probabilistic objective function and constraints. The network has multiple sources as well as demands with varied distributions. In order to find an overall distribution for probability density estimation, a decomposition scheme is applied where each scenario is solved separately as a sub-problem. The solution from sub-problem is used to find the probability using kernel density estimator. The modified model equations lead to a stochastic MINLP problem. The proposed framework and optimization formulations are applied to a case study in the Mexican state of Sonora. From the demand and natural resource distribution, 12 scenarios have been identified and the model is weighted by their probability of occurrence. The results are compared with the deterministic solution to obtain the cost associated with uncertainty.

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Section: Uncertainty In the Water Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Design of Water Distribution Networks with Incorporation of Uncertainties and Energy Nexus

Mukherjee¹,

González-Bravo

Nápoles‐Rivera

et al. 2017

Process Integr Optim Sustain

View full text Add to dashboard Cite

show abstract

“…To do so, the following nonlinear, stochastic mixed -3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 integer programming problem is formulated where the objective is to maximize the overall FI, subject to the costs associated with placing a network of on-line sensors. (2) Subject to (3)…”

Section: Fisher Information Maximizationmentioning

confidence: 99%

“…But a much more effective solution to this issue is provided by the BONUS (Better Optimization for Nonlinear Uncertain Systems) algorithm which is based on a reweighting scheme, a form of Reduced Order Model (ROM) in probability space. For more details of this algorithm, please refer to Diwekar and David (2).…”

Section: Bonus Algorithmmentioning

confidence: 99%

“…Hence, the problem of sensor placement is formulated as a multi-objective stochastic programming problem, the solution of which is the optimal sensor deployment policy for a specific advanced power system in the face of uncertainties in performance, efficiency, and power demand. In order to solve this large scale multi-objective optimization under uncertainty problem, a new algorithmic framework based on the Better Optimization of Nonlinear Uncertain Systems (BONUS) algorithm (1,2) and a new multi-objective optimization algorithm developed specifically for this problem are used. This is the first attempt at the problem of optimal sensor deployment in advanced power plants with consideration of hybrid hardware and virtual sensing and with the incorporation of uncertainty with multiple objectives.…”

Section: Introductionmentioning

confidence: 99%

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A multi-objective optimization approach to optimal sensor location problem in IGCC power plants

Sen

Diwekar

2016

Applied Energy

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Integrated Gasification Combined Cycle (IGCC) power plants provide a cleaner and more efficient way to obtain energy from coal. In order to operate an IGCC power plant in a safe and stable manner, several input and output process parameters need to be monitored. However, due to economic and operational constraints, it is infeasible to place sensors at every input and output process parameter location. Hence, it becomes important to select the most effective sensor locations which lead to maximum information gain about the plant conditions. Practical issues present in an IGCC power plant, such as harsh physical conditions and variability in process parameters, make the optimal sensor location problem an especially complicated one. Further, sensors can have multiple objectives and they can produce uncertainties due to measurement errors. This work considers hybrid hardware and virtual sensing for advanced power systems with multiple objectives. In order to solve this real world large scale problem, we use a novel algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS). BONUS works in probability distribution space and avoids sampling for each optimization and derivative calculations iterations. A new algorithm for multi-objective optimization is also developed specifically for problem. The result of this nonlinear stochastic multi-objective problem is the non-dominated, or Pareto set, which provides trade-offs between various objectives like observability, cost, and thermal efficiency. This is the first attempt at the problem of optimal sensor deployment in advanced power plants, with consideration of hybrid hardware and virtual sensing and incorporation of uncertainty with multiple objectives.

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Sampling Techniques

Acikgoz

Diwekar

2017

Kirk-Othmer Encyclopedia of Chemical Technology

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Sampling is a statistical procedure used for uncertainty analysis and stochastic modeling in many applications related to process design, operation, control and optimization. The increased efforts for pollution prevention and sustainability necessitate a more contemporary approach for process design, in which objectives such as reducing environmental and health impacts, increasing reliability, safety and controllability, reducing risk and achieving better profitability are considered simultaneously. In this multifaceted approach to process design and operation, uncertainties need to be considered and included in process models and simulations. These uncertainties can be static or dynamic. A review of sampling techniques such as Monte Carlo sampling, importance sampling, Latin Hypercube Sampling (LHS) and Hammersley Sequence Sampling (HSS) are provided here. These sampling techniques have a wide range of use in chemical and process synthesis, process scheduling, supply chain management, process control and reliability as well as risk management. Apart from uncertainty analysis, sampling and sampling accuracy plays an important role in discrete, stochastic, and multi‐objective optimization algorithms. This review also describes the recent developments in this area. In addition, future trends in process design and sampling techniques are also presented.

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BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems

Abstract: The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Cited by 21 publications

References 0 publications

Optimal Design of Water Distribution Networks with Incorporation of Uncertainties and Energy Nexus

Optimal Design of Water Distribution Networks with Incorporation of Uncertainties and Energy Nexus

A multi-objective optimization approach to optimal sensor location problem in IGCC power plants

Sampling Techniques

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