Winnie Yee

Determining the Cost of Producing Ethanol from Corn Starch and Lignocellulosic Feedstocks

The mature corn-to-ethanol industry has many similarities to the emerging lignocellulose-to-ethanol industry. It is certainly possible that some of the early practitioners of this new technology will be the current corn ethanol producers. In order to begin to explore synergies between the two industries, a joint project between two agencies responsible for aiding these technologies in the Federal government was established. This joint project of the USDA-ARS and DOE/NREL looked at the two processes on a similar process design and engineering basis, and will eventually explore ways to combine them. This report describes the comparison of the processes, each producing 25 million annual gallons of fuel ethanol. This paper attempts to compare the two processes as mature technologies, which requires assuming that the technology improvements needed to make the lignocellulosic process commercializable are achieved, and enough plants have been built to make the design well-understood. Ass umptions about yield and design improvements possible from continued research were made for the emerging lignocellulose process. In order to compare the lignocellulose-to-ethanol process costs with the commercial corn-to-ethanol costs, it was assumed that the lignocellulose plant was an Nth generation plant, built after the industry had been sufficiently established to eliminate first-of-a-kind costs. This places the lignocellulose plant costs on a similar level with the current, established corn ethanol industry, whose costs are well known. The resulting costs of producing 25 million annual gallons of fuel ethanol from each process were determined. The figure below shows the production cost breakdown for each process. The largest cost contributor in the corn starch process is the feedstock; for the lignocellulosic process it is the capital cost, which is represented by depreciation cost on an annual basis.

Feasibility study for co-locating and integrating ethanol production plants from corn starch and lignocellulosic feedstocks

Analysis of the feasibility of co-locating corn-grain-to-ethanol and lignocellulosic ethanol plants and potential savings from combining utilities, ethanol purification, product processing, and fermentation. Although none of the scenarios identified could produce ethanol at lower cost than a straight grain ethanol plant, several were lower cost than a straight cellulosic ethanol plant.

Economic analysis of fuel ethanol production from winter hulled barley by the EDGE (Enhanced Dry Grind Enzymatic) process.

A process and cost model was developed for fuel ethanol production from winter barley based on the EDGE (Enhanced Dry Grind Enzymatic) process. In this process, in addition to β-glucanases, which are added to reduce the viscosity of the mash, β-glucosidase is also added to completely hydrolyze the oligomers obtained during the hydrolysis of β-glucans to glucose. The model allows determination of capital costs, operating costs, and ethanol production cost for a plant producing 40 million gallons of denatured fuel ethanol annually. A sensitivity study was also performed to examine the effects of β-glucosidase and barley costs on the final ethanol production cost. The results of this study clearly demonstrate the economic benefit of adding β-glucosidase. Lower ethanol production cost was obtained compared to that obtained without β-glucosidase addition in all cases except one where highest β-glucosidase cost allowance and lowest barley cost were used.

Fractionation of Whey Protein Isolate with Supercritical Carbon Dioxide—Process Modeling and Cost Estimation

An economical and environmentally friendly whey protein fractionation process was developed using supercritical carbon dioxide (sCO2) as an acid to produce enriched fractions of α-lactalbumin (α-LA) and β-lactoglobulin (β-LG) from a commercial whey protein isolate (WPI) containing 20% α-LA and 55% β-LG, through selective precipitation of α-LA. Pilot-scale experiments were performed around the optimal parameter range (T = 60 to 65 °C, P = 8 to 31 MPa, C = 5 to 15% (w/w) WPI) to quantify the recovery rates of the individual proteins and the compositions of both fractions as a function of processing conditions. Mass balances were calculated in a process flow-sheet to design a large-scale, semi-continuous process model using SuperproDesigner® software. Total startup and production costs were estimated as a function of processing parameters, product yield and purity. Temperature, T, pressure, P, and concentration, C, showed conflicting effects on equipment costs and the individual precipitation rates of the two proteins, affecting the quantity, quality, and production cost of the fractions considerably. The highest α-LA purity, 61%, with 80% α-LA recovery in the solid fraction, was obtained at T = 60 °C, C = 5% WPI, P = 8.3 MPa, with a production cost of

Computer simulation of energy use, greenhouse gas emissions, and process economics of the fluid milk process1

8.65 per kilogram of WPI treated. The most profitable conditions resulted in 57%-pure α-LA, with 71% α-LA recovery in the solid fraction and 89% β-LG recovery in the soluble fraction, and production cost of

A process model to estimate biodiesel production costs

5.43 per kilogram of WPI treated at T = 62 °C, C = 10% WPI and P = 5.5 MPa. The two fractions are ready-to-use, new food ingredients with a pH of 6.7 and contain no residual acid or chemical contaminants.

Engineering process and cost model for a conventional corn wet milling facility

Energy-savings measures have been implemented in fluid milk plants to lower energy costs and the energy-related carbon dioxide (CO2) emissions. Although these measures have resulted in reductions in steam, electricity, compressed air, and refrigeration use of up to 30%, a benchmarking framework is necessary to examine the implementation of process-specific measures that would lower energy use, costs, and CO2 emissions even further. In this study, using information provided by the dairy industry and equipment vendors, a customizable model of the fluid milk process was developed for use in process design software to benchmark the electrical and fuel energy consumption and CO2 emissions of current processes. It may also be used to test the feasibility of new processing concepts to lower energy and CO2 emissions with calculation of new capital and operating costs. The accuracy of the model in predicting total energy usage of the entire fluid milk process and the pasteurization step was validated using available literature and industry energy data. Computer simulation of small (40.0 million L/yr), medium (113.6 million L/yr), and large (227.1 million L/yr) processing plants predicted the carbon footprint of milk, defined as grams of CO2 equivalents (CO2e) per kilogram of packaged milk, to within 5% of the value of 96 g of CO 2e/kg of packaged milk obtained in an industry-conducted life cycle assessment and also showed, in agreement with the same study, that plant size had no effect on the carbon footprint of milk but that larger plants were more cost effective in producing milk. Analysis of the pasteurization step showed that increasing the percentage regeneration of the pasteurizer from 90 to 96% would lower its thermal energy use by almost 60% and that implementation of partial homogenization would lower electrical energy use and CO2e emissions of homogenization by 82 and 5.4%, respectively. It was also demonstrated that implementation of steps to lower non-process-related electrical energy in the plant would be more effective in lowering energy use and CO2e emissions than fuel-related energy reductions. The model also predicts process-related water usage, but this portion of the model was not validated due to a lack of data. The simulator model can serve as a benchmarking framework for current plant operations and a tool to test cost-effective process upgrades or evaluate new technologies that improve the energy efficiency and lower the carbon footprint of milk processing plants.