Optimization models for integrated biorefinery operations

Gulcan, Berkay; Ekşioğlu, Sandra D.; Song, Yongjia; Roni, Mohammad; Chen, Qiushi

doi:10.1007/s11590-021-01767-4

Cited by 4 publications

(5 citation statements)

References 42 publications

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“…The optimization framework could also include other promising biomass conversion routes and unit operations that are not modeled in the Aspen Plus. Rigorous kinetic and differential equation models or measurements on actual equipment could provide the raw material flow rate, utility usage, and product generation in those cases . Both profitability and environmental benefits are essential considerations for the design of integrated processes with multiple feedstock and product choices, which requires a biobjective optimization model.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Although pilot plant can help reducing the process performance uncertainties during scale-up, it is important to screen promising technologies in the early stage to save time and resources. In this case, benchtop experimental yields and separation efficiencies are utilized to larger scale plants, which is subject to intrinsic variations, inaccurate measurement, and unexpected scale-up engineering problems. , The feedstock supply, composition, and chemical prices are volatile and could affect plant profitability . Ignoring these uncertainties may render the designs and operations suboptimal or even infeasible. , Stochastic programming is commonly used to incorporate uncertain parameter distributions as scenarios .…”

Section: Introductionmentioning

confidence: 99%

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Multifeedstock and Multiproduct Process Design Using Neural Network Surrogate Flexibility Constraints

Luo

Ierapetritou

2023

Ind. Eng. Chem. Res.

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Biorefineries are designed to utilize a combination of various technologies to transform biomass derived raw materials into different valueadded products. This strategy has been highlighted in the literature for reducing waste, increasing profitability, and improving the process resilience to uncertain biomass feedstocks. In this work, a two-stage stochastic programming (TSSP) model is developed to maximize profit and minimize emissions under different sources of uncertainties. Data-driven surrogate models are built for biorefinery's flexibility index (FI) to quantify and improve its operational flexibility. The neural network with rectified linear unit (ReLU) activation function is established as the appropriate surrogate model because it closely approximates the flexibility index while retaining the mixed-integer linear characteristics of the overall design formulation. Moreover, the stochastic programming demonstrates the magnitude of environmental impact uncertainty quantitatively in each scenario using empirical price/demand/supply uncertainty information, which cannot be addressed by the traditional Pedigree-based life cycle assessment (LCA) uncertainty analysis.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multifeedstock and Multiproduct Process Design Using Neural Network Surrogate Flexibility Constraints

Luo

Ierapetritou

2023

Ind. Eng. Chem. Res.

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“…Different from these works, our research models the uncertainty of biomass moisture level. In a recent work, we proposed a chance-constrained SP model in (Gulcan et al 2021) that identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. This work considers the uncertainty of biomass moisture content, particle size uncertainties, and random equipment failure.…”

Section: Optimization Of Biorefinery Operationsmentioning

confidence: 99%

“…We develop a case study using historical data about the characteristics of biomass and the performance of equipment of PDU (Gulcan et al 2021). We also consulted the experts and operators of the PDU during model development, verification, and validation.…”

Section: Data Descriptionmentioning

confidence: 99%

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A Multi-stage Stochastic Programming Model for Adaptive Biomass Processing Operation under Uncertainty

Gulcan¹,

Song²,

Ekşioğlu³

2021

Preprint

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Variations of physical and chemical characteristics of biomass reduce equipment utilization and increase operational costs of biomass processing. Biomass processing facilities use sensors to monitor the changes in biomass characteristics. Integrating sensory data into the operational decisions in biomass processing will increase its flexibility to the changing biomass conditions. In this paper, we propose a multi-stage stochastic programming model that minimizes the expected operational costs by identifying the initial inventory level and creating an operational decision policy for equipment speed settings. These policies take the sensory information data and the current biomass inventory level as inputs to dynamically adjust inventory levels and equipment settings according to the changes in the biomass' characteristics. We ensure that a prescribed target reactor utilization is consistently achieved by penalizing the violation of the target reactor feeding rate. A case study is developed using real-world data collected at Idaho National Laboratory's biomass processing facility. We show the value of multi-stage stochastic programming from an extensive computational experiment. Our sensitivity analysis indicates that updating the infeed rate of the system, the processing speed of equipment, and bale sequencing based on the moisture level of biomass improves the processing rate of the reactor and reduces operating costs.

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A multi-stage stochastic programming model for adaptive biomass processing operation under uncertainty

et al. 2022

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Optimization models for integrated biorefinery operations

Cited by 4 publications

References 42 publications

Multifeedstock and Multiproduct Process Design Using Neural Network Surrogate Flexibility Constraints

Multifeedstock and Multiproduct Process Design Using Neural Network Surrogate Flexibility Constraints

A Multi-stage Stochastic Programming Model for Adaptive Biomass Processing Operation under Uncertainty

A multi-stage stochastic programming model for adaptive biomass processing operation under uncertainty

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