Throughput, Reliability, and Yields of a Pilot-Scale Conversion Process for Production of Fermentable Sugars from Lignocellulosic Biomass: A Study on Feedstock Ash and Moisture

Abstract

Early lignocellulosic biorefineries have been plagued with numerous issues that involve feedstock handling problems and variations in conversion efficacy that stem from feedstock variability and complexity in dimensional, physical, chemical, and mechanical attributes. Feedstock ash and moisture content vary considerably in corn stover harvested from farms for bioconversion, and their effects on preprocessing (grinding/milling) and subsequent chemical and enzymatic conversion to fermentable sugars is systematically explored here using pilot-scale hammer mill grinders and a chemical hydrolysis reactor. Corn stover with high ash content due to contamination from soil was found to (1) consume higher power during grinding resulting in reductions of processing rates and (2) produce a larger fraction of fines in the feedstock that were lost to dust mitigation systems causing higher mechanical wear rates. Corn stover feedstock coming from fields with a high residual moisture content resulting in bale degradation due to self-heating caused a more pronounced drop in preprocessing throughput due to grinder overloads and process upsets leading to equipment downtime. Conversion yield to sugars was not affected, although differences in fermentation performance on these sugar streams was not examined. The overall process throughput was only 40−70% of nameplate capacity due to preprocessing problems.

Image Analysis for Rapid Assessment and Quality-Based Sorting of Corn Stover

“…For the samples with variable ash content, samples were selected with ash content of 5-10% (low ash) and 10-20% (high ash) (Sievers et al, 2020), surface roughness calculated as Rq (Figure 9A), and the GLCM parameter Shade (Figure 9C) appear to distinguish between high and low ash samples. The pattern, however, was different between Rq and Shade, with the high ash sample measuring low Rq and higher Shade values.…”

“…Variations in lignocellulosic biomass material and quality attributes are often overlooked when assessing feedstock value and pathways for conversion to fuels, chemicals, and products (Ray et al, 2020). Variations in cell wall composition, extractives, moisture content, inorganic species, and soil contaminants have been identified as critical factors affecting biomass quality, process uptime, and product yields (Ray et al, 2020;Sievers et al, 2020;Ding et al, 2021a). Hoover et al (2019) developed several multiple regression models where five chemical characteristics could be used to estimate biochemical conversion performance.…”

Real-time biomass feedstock particle quality detection using image analysis and machine vision

“…We operated a 500 kg/d-rated pilot plant at the National Renewable Energy Laboratory (NREL) Integrated Bioenergy Research Facility over four 50-h experimental runs to convert milled corn stover into fermentable sugars via a dilute-acid chemical hydrolysis and to observe equipment operability and performance [3]. Feeding equipment upstream (colored orange in Fig.…”

“…Specifically, the size and shape of material can play a major role in the flow properties that make gravity-fed and even force-fed hoppers and conveyors function inconsistently or fail altogether [2]. Universal equipment that works on more conventional materials such as cereal grains can experience bridging, jamming, and motor load spikes when trying to covey lignocellulosic feedstocks such as corn stover [3]. New equipment designs specific to the feedstock properties may be necessary to alleviate some of these challenges, and additional solutions to cope with varying feedstock qualities may include the application of advanced process controls to adjust equipment and even reject some feedstocks that do not meet a quality standard-both of which require online instrumentation for real-time measurement [4].…”

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

“…Moreover, differences in the physical properties of individual corn stover particles are largely derived from anatomical differences in the tissues. Such differences can impact the mechanical handling of biomass, have been shown to contribute to process upsets and can be detrimental to overall process throughput (Sievers et al, 2020). Since different anatomical tissues respond differently to both preprocessing (e.g., comminution) and deconstruction (e.g., chemical pretreatment and enzymatic hydrolysis), on-line knowledge of the tissue type, composition, and moisture content could prove to be a fundamental requirement for commercial-scale biorefineries (Garlock et al, 2009;Crowe et al, 2017;Li et al, 2018).…”