Matthew J. Realff

Cellulose crystallinity--a key predictor of the enzymatic hydrolysis rate.

The enzymatic hydrolysis of cellulose encounters various limitations that are both substrate‐ and enzyme‐related. Although the crystallinity of pure cellulosic Avicel plays a major role in determining the rate of hydrolysis by cellulases from Trichoderma reesei, we show that it stays constant during enzymatic conversion. The mode of action of cellulases was investigated by studying their kinetics on cellulose samples. A convenient method for reaching intermediate degrees of crystallinity with Avicel was therefore developed and the initial rate of the cellulase‐catalyzed hydrolysis of cellulose was demonstrated to be linearly proportional to the crystallinity index of Avicel. Despite correlation with the adsorption capacity of cellulases onto cellulose, at a given enzyme loading, the initial enzymatic rate continued to increase with a decreasing crystallinity index, even though the bound enzyme concentration stayed constant. This finding supports the determinant role of crystallinity rather than adsorption on the enzymatic rate. Thus, the cellulase activity and initial rate data obtained from various samples may provide valuable information about the details of the mechanistic action of cellulase and the hydrolysable/reactive fractions of cellulose chains. X‐ray diffraction provides insight into the mode of action of Cel7A from T. reesei. In the conversion of cellulose, the (021) face of the cellulose crystal was shown to be preferentially attacked by Cel7A from T. reesei.

Modeling cellulase kinetics on lignocellulosic substrates

The enzymatic hydrolysis of cellulose to glucose by cellulases is one of the major steps involved in the conversion of lignocellulosic biomass to yield biofuel. This hydrolysis by cellulases, a heterogeneous reaction, currently suffers from some major limitations, most importantly a dramatic rate slowdown at high degrees of conversion. To render the process economically viable, increases in hydrolysis rates and yields are necessary and require improvement both in enzymes (via protein engineering) and processing, i.e. optimization of reaction conditions, reactor design, enzyme and substrate cocktail compositions, enzyme recycling and recovery strategies. Advances in both areas in turn strongly depend on the progress in the accurate quantification of substrate-enzyme interactions and causes for the rate slowdown. The past five years have seen a significant increase in the number of studies on the kinetics of the enzymatic hydrolysis of cellulose. This review provides an overview of the models published thus far, classifies and tabulates these models, and presents an analysis of their basic assumptions. While the exact mechanism of cellulases on lignocellulosic biomass is not completely understood yet, models in the literature have elucidated various factors affecting the enzymatic rates and activities. Different assumptions regarding rate-limiting factors and basic substrate-enzyme interactions were employed to develop and validate these models. However, the models need to be further tested against additional experimental data to validate or disprove any underlying hypothesis. It should also provide better insight on additional parameters required in the case that more substrate and enzyme properties are to be included in a model.

Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty

Abstract Bio-fuels represent promising candidates for renewable liquid fuels. One of the challenges for the emerging industry is the high level of uncertainty in supply amounts, market demands, market prices, and processing technologies. These uncertainties complicate the assessment of investment decisions. This paper presents a model for the optimal design of biomass supply chain networks under uncertainty. The uncertainties manifest themselves as a large number of stochastic model parameters that could impact the overall profitability and design. The supply chain network we study covers the Southeastern region of the United States and includes biomass supply locations and amounts, candidate sites and capacities for two kinds of fuel conversion processing, and the logistics of transportation from the locations of forestry resources to the conversion sites and then to the final markets. To reduce the design problem to a manageable size the impact of each uncertain parameter on the objective function is computed for each end of the parameters range. The parameters that cause the most change in the profit over their range are then combined into scenarios that are used to find a design through a two stage mixed integer stochastic program. The first stage decisions are the capital investment decisions including the size and location of the processing plants. The second stage recourse decisions are the biomass and product flows in each scenario. The objective is the maximization of the expected profit over the different scenarios. The robustness and global sensitivity analysis of the nominal design (for a single nominal scenario) vs. the robust design (for multiple scenarios) are analyzed using Monte Carlo simulation over the hypercube formed from the parameter ranges.

Assessing performance and uncertainty in developing carpet reverse logistics systems

The US carpet industry is striving to reach a 40% diversion rate from landfills by 2012, according to a memorandum of understanding signed by industry and government officials in 2002. As a result, they are interested in methods of setting up a reverse logistics (RL) system which will allow them to manage the highly variable return flows. In this paper, we simulate such a carpet RL supply chain and use a designed experiment to analyze the impact of the system design factors as well as environmental factors impacting the operational performance of the RL system. First, we identify the relative importance of various network design parameters. We then show that even with the design of an efficient RL system, the use of better recycling technologies, and optimistic growth in recycling rates, the return flows cannot meet demand for nearly a decade. We conclude by discussing possible management options for the carpet industry to address this problem, including legal responses to require return flows and the use of market incentives for recycling.

Robust reverse production system design for carpet recycling

It is estimated that complete carpet recycling would avoid an estimated US annual landfill cost of

Multivariate statistical analysis of X-ray data from cellulose: A new method to determine degree of crystallinity and predict hydrolysis rates

65 million, while simultaneously recovering lost material valued at

Carpet Recycling: Determining the Reverse Production System Design

750 million. Designing an adequate reverse production system is critical to the economic viability of recovering this lost value. We develop a robust-mixed-integer linear programming model to support decision-making for reverse production infrastructure design. Our robust model seeks solutions close to the mathematically optimal solutions for a set of alternative scenarios identified by a decision-maker. To demonstrate the approach, a representative industrial case study is given for a large-scale carpet recycling problem. A robust solution is found that appraises the impact of two major sources of uncertainty, volumes of carpet collected and price of a key recycled material.

Life Cycle Energy and Greenhouse Gas Emissions for an Ethanol Production Process Based on Blue-Green Algae

The enzymatic hydrolysis of cellulose by cellulases is one of the major steps in the production of ethanol from lignocellulosics. However, cellulosic biomass is not particularly susceptible to enzymatic attack and crystallinity of the substrates is one of the key properties that determine the hydrolysis rates. In this work, by quantifying the respective contributions of amorphous and crystalline cellulose to the X-ray diffraction spectra of cellulose with intermediate degrees of crystallinity, a new method to obtain consistent crystallinity index values was developed. Multivariate statistical analysis was applied to spectra obtained from phosphoric acid pretreated cellulose samples of various intermediate (but undetermined) crystallinity indices to reduce their dimensionality. The crystallinity indices obtained were found to be linearly related to the enzymatic hydrolysis rates. The method was validated by predicting the degree of crystallinity of samples containing various ratios of microcrystalline cellulose and amorphous cellulose, both of known crystallinity indices. Dimensionality reduction of the spectra was also used to predict the enzymatic hydrolysis rates of various cellulose samples from X-ray data. The method developed in this work could be generalized to accurately assess the degree of crystallinity for a wide range of varieties of cellulose.

Strategic design of reverse production systems

Abstract Roughly 4 billion pounds of carpet are disposed of in the United States each year. This carpet is composed of a significant fraction of nylon, polypropylene, and polyester fiber. A key limiting factor to recycling is effective design and development of the reverse production system to collect and reprocess this large volume of valuable material. A reverse production system is composed of material and chemical recycling functional elements interconnected by transportation steps. In this article, we develop a mixed-integer programming model to support decision-making in reverse production system design. To illustrate its use and applicability, we apply the model to a representative U.S. carpet recycling industrial case study. The overall economic feasibility of recycling is strongly dependent on the volumes that can be expected from investments in collection infrastructure. The geographic location of processing centers influences the network economics, and the subdivision of recycling tasks to avoi...

Dynamic programming in a heuristically confined state space: a stochastic resource-constrained project scheduling application

Ethanol can be produced via an intracellular photosynthetic process in cyanobacteria (blue-green algae), excreted through the cell walls, collected from closed photobioreactors as a dilute ethanol-in-water solution, and purified to fuel grade ethanol. This sequence forms the basis for a biofuel production process that is currently being examined for its commercial potential. In this paper, we calculate the life cycle energy and greenhouse gas emissions for three different system scenarios for this proposed ethanol production process, using process simulations and thermodynamic calculations. The energy required for ethanol separation increases rapidly for low initial concentrations of ethanol, and, unlike other biofuel systems, there is little waste biomass available to provide process heat and electricity to offset those energy requirements. The ethanol purification process is a major consumer of energy and a significant contributor to the carbon footprint. With a lead scenario based on a natural-gas-fueled combined heat and power system to provide process electricity and extra heat and conservative assumptions around the ethanol separation process, the net life cycle energy consumption, excluding photosynthesis, ranges from 0.55 MJ/MJ(EtOH) down to 0.20 MJ/ MJ(EtOH), and the net life cycle greenhouse gas emissions range from 29.8 g CO₂e/MJ(EtOH) down to 12.3 g CO₂e/MJ(EtOH) for initial ethanol concentrations from 0.5 wt % to 5 wt %. In comparison to gasoline, these predicted values represent 67% and 87% reductions in the carbon footprint for this ethanol fuel on a energy equivalent basis. Energy consumption and greenhouse gas emissions can be further reduced via employment of higher efficiency heat exchangers in ethanol purification and/ or with use of solar thermal for some of the process heat.