Unraveling Optimal Biomass Processing Routes from Bioconversion Product and Process Networks under Uncertainty: An Adaptive Robust Optimization Approach

Gong, Jian; Garcia, Daniel J.; You, Fengqi

doi:10.1021/acssuschemeng.6b00188

Cited by 79 publications

(31 citation statements)

References 70 publications

(107 reference statements)

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“…U is an uncertainty set that characterizes the region of uncertainty realizations. ARO approaches could be applied to address uncertainty in a variety of applications, including process design [99][100][101], process scheduling [102], supply chain optimization [101,103], among others.…”

Section: Robust Optimizationmentioning

confidence: 99%

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Ning

You

2019

Computers & Chemical Engineering

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253

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This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. A comprehensive review and classification of the relevant publications on data-driven distributionally robust optimization, data-driven chance constrained program, data-driven robust optimization, and data-driven scenario-based optimization is then presented. This paper also identifies fertile avenues for future research that focuses on a closed-loop data-driven optimization framework, which allows the feedback from mathematical programming to machine learning, as well as scenario-based optimization leveraging the power of deep learning techniques. Perspectives on online learningbased data-driven multistage optimization with a learning-while-optimizing scheme is presented.

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Section: Robust Optimizationmentioning

confidence: 99%

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Ning

You

2019

Computers & Chemical Engineering

Self Cite

253

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“…h2Tx2Mu. In the following, we will provide the details on reformulation of (12). The key techniques include representing the uncertainty in a convex combination of the extreme points of uncertainty set and the Glover's linearization.…”

Section: The Tailored Column-and-constraint Generation Algorithmmentioning

confidence: 99%

“…[3][4][5][6] Among these methods, robust optimization (RO) emerges as a popular approach due to its strong ability to hedge against uncertainties and also because of its computational tractability. [7][8][9][10][11] RO has a broad array of successful applications in process systems engineering, including process design and synthesis, [12][13][14] process scheduling, [15][16][17][18] and supply chain optimization. [19][20][21] Traditional RO approaches, also known as static robust optimization (SRO), 8 make all the decisions at once.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven adaptive nested robust optimization: General modeling framework and efficient computational algorithm for decision making under uncertainty

2017

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A novel data‐driven adaptive robust optimization framework that leverages big data in process industries is proposed. A Bayesian nonparametric model—the Dirichlet process mixture model—is adopted and combined with a variational inference algorithm to extract the information embedded within uncertainty data. Further a data‐driven approach for defining uncertainty set is proposed. This machine‐learning model is seamlessly integrated with adaptive robust optimization approach through a novel four‐level optimization framework. This framework explicitly accounts for the correlation, asymmetry and multimode of uncertainty data, so it generates less conservative solutions. Additionally, the proposed framework is robust not only to parameter variations, but also to anomalous measurements. Because the resulting multilevel optimization problem cannot be solved directly by any off‐the‐shelf solvers, an efficient column‐and‐constraint generation algorithm is proposed to address the computational challenge. Two industrial applications on batch process scheduling and on process network planning are presented to demonstrate the advantages of the proposed modeling framework and effectiveness of the solution algorithm. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3790–3817, 2017

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“…The bilinear terms are products of dual variables and level of deviation variables, including

P 13 \cdot T 6

P 1 4_{i, j, k} \cdot T 7_{k}

, and

P 1 5_{k} \cdot T 8_{k}

\forall i \in I, j \in J, k \in K

. Since we consider integer budgets of uncertainty, continuous variable T6 and T7 k can be reduced to binary variables and the related bilinear terms take non‐positive values given that both P13 and P14 i,j,k are non‐negative variables . As a result, we apply the Glover's linearization scheme for the simplified bilinear terms as in Constraints (74)–(79) .…”

Section: Solution Strategymentioning

confidence: 99%

Optimal processing network design under uncertainty for producing fuels and value‐added bioproducts from microalgae: Two‐stage adaptive robust mixed integer fractional programming model and computationally efficient solution algorithm

Gong

You

2016

AIChE Journal

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Fractional metrics, such as return on investment (ROI), are widely used for performance evaluation, but uncertainty in the real market may unfortunately diminish the results that are based on nominal parameters. This article addresses the optimal design of a large‐scale processing network for producing a variety of algae‐based fuels and value‐added bioproducts under uncertainty. We develop by far the most comprehensive processing network with 46,704 alternative processing pathways. Based on the superstructure, a two‐stage adaptive robust mixed integer fractional programming model is proposed to tackle the uncertainty and select the robust optimal processing pathway with the highest ROI. Since the proposed problem cannot be solved directly by any off‐the‐shelf solver, we develop an efficient tailored solution method that integrates a parametric algorithm with a column‐and‐constraint generation algorithm. The resulting robust optimal processing pathway selects biodiesel and poly‐3‐hydroxybutyrate as the final fuel and bioproduct, respectively. © 2016 American Institute of Chemical Engineers AIChE J, 63: 582–600, 2017

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Unraveling Optimal Biomass Processing Routes from Bioconversion Product and Process Networks under Uncertainty: An Adaptive Robust Optimization Approach

Cited by 79 publications

References 70 publications

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Data‐driven adaptive nested robust optimization: General modeling framework and efficient computational algorithm for decision making under uncertainty

Optimal processing network design under uncertainty for producing fuels and value‐added bioproducts from microalgae: Two‐stage adaptive robust mixed integer fractional programming model and computationally efficient solution algorithm

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