Hot Water Extracted and Non-extracted Willow Biomass Storage Performance: Fuel Quality Changes and Dry Matter Losses

Obste Therasme,Timothy A Volk,Hein San,Nasheett Usman,Mark H Eisenbies

doi:10.3389/fenrg.2019.00165

Abstract

Dry matter losses (DML) and fuel quality changes occurring in storage piles are important parameters for the management of any biomass supply system. This study evaluates the effect of a hot water extraction pretreatment, harvest season, depth in storage pile and initial moisture content on willow biomass fuel quality (moisture, ash, higher (HHV) and lower (LHV) heating value) during storage, and models DML in storage piles based on experimental data. For the summer storage (SS) pile, mesh bags containing freshly harvested chips (FC) were inserted at 0.5-1 meter deep in the pile. For the winter storage pile (WS), the mesh bags were filled with FC and hot water extracted chips (HC) with three different initial moisture contents inserted in the shell (<0.45 cm) and the core (1-1.5m) of the pile. The ash contents through all sampling periods were in the range of 1.1 to 2.2% for FC and 0.6 to 2.1% for HC from both the shell and core of the WS pile. Higher ash contents, in the range of 2.1 to 3.4%, were observed in SS pile. Moisture contents of the storage piles had differing patterns over time. DML was the highest in the SS pile, reaching up to 33.6% after 140 days in storage; in contrast, there was no significant increase in DML over the first winter season. Although DML of FC and HC were in the same range during the initial storage period, DML of HC was 40% lower than FC after 180 days of storage. Higher DML was observed in the core (e.g. 17.3% for FC) compared to the shell (e.g. 12.1% for FC) at the end of the WS trial. There was no particular trend observed between initial moisture and DML. This study suggests that a linear model is sufficient to estimate DML, but a nonlinear model may be needed for chips stored in SS piles for six months or longer. It also suggests that DML is reduced in storage piles created in winter, and that willow chips kept in SS should be utilized within two months for a DML below a 10% threshold.

Highlights

Short rotation woody crops (SRWC), such as shrub willow (Salix spp.) and hybrid poplar (Populus spp.), are being developed in North America and Europe for bioenergy (Mola-Yudego et al, 2016; Volk et al, 2016, 2018)
This study determined the changes in dry matter loss, moisture, ash, and heating value during the storage of shrub willow chips in piles built in summer and winter
Of the two storage pile experiments, it was shown that the rate of Dry matter loss (DML) was higher in summer storage (SS) piles than winter storage pile (WS) piles; DML in a SS pile increased at a rate of 7.1% per month during the first summer and fall season of the storage, decreased to a rate of 0.7% in the spring and the following summer while DML in a WS pile created with freshly harvested willow chips increased by only 1.0% per month in the shell and 2.6% in the core of the pile

Summary

Introduction

Short rotation woody crops (SRWC), such as shrub willow (Salix spp.) and hybrid poplar (Populus spp.), are being developed in North America and Europe for bioenergy (Mola-Yudego et al, 2016; Volk et al, 2016, 2018). Storage is an essential component of the biomass supply system because harvesting windows are limited while demand for feedstock for power, biofuels and biobased chemicals is required year-round. Dry matter loss (DML) can occur at multiple places along the supply chain such as during harvesting, loading and unloading of vehicles, transportation, and storage. DML is an important factor as it can translate into loss of revenue (Routa et al, 2018; Therasme et al, 2019), waste of resources, higher greenhouse gas emissions per unit of energy delivered, and challenges at conversion facilities. A DML of 10% during the storage of willow biomass for heat production would result in 6% increase of greenhouse gas emissions per unit of delivered heat and 9% decrease of the net energy yield per unit of land (Whittaker et al, 2016)

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