Stochastic Process Optimization of an Integrated Biorefinery

TL;DR

This paper develops a stochastic optimization model for biorefinery feedstock blending and operation, accounting for biomass variability, and demonstrates its effectiveness through numerical analysis and managerial insights.

Contribution

It introduces a novel stochastic optimization approach with SAA for biorefinery planning considering biomass variability, improving process robustness and efficiency.

Findings

Sequencing bales by moisture and carbohydrate content improves processing robustness.

The proposed model enhances reactor processing time and rate.

Managerial insights facilitate practical implementation.

Abstract

Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed.