ASSESSING THE IMPACT OF UNCERTAINTY ON ETHANOL PRODUCTION OUTCOMES

ASSESSING THE IMPACT OF UNCERTAINTY ON ETHANOL PRODUCTION OUTCOMES A Master of Engineering Project Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Master of Engineering Mariel B Eisenberg August 2011 Assessing the Impact of Uncertainty on Ethanol Production Outcomes Mariel B Eisenberg Department of Biological and Environmental Engineering Cornell University August 2011 As research into cellulosic ethanol production advances, efficiencies are improving at every step through the process Review of relevant research shows significant variability in parameter estimates for almost every unit process through both supply chain and conversion process The objective of this study is an assessment of the impact of various parametric uncertainties on the overall material requirements for ethanol production, specifically feedstock requirements and land area for production of feedstock The analysis is based on a generalized input-output style model of ethanol production, with uncertainty introduced through a Monte Carlo Simulation (MCS) framework In the initial study, uncertainties in crop yield, storage loss, sugar yield, and fermentation yield are considered Results show the variation of crop yield has the greatest effect on land area requirements; while variation of sugar yield has the greatest effect on harvested switchgrass, given crop yield parameters Further analysis will consider the impact of these uncertainties on economic and energy flows in the system ii ACKNOWLEDGEMENTS I would like to thank my advisor, Lindsay Anderson, for her guidance throughout the research process Thank you to Professor Larry Walker for providing the switchgrass to ethanol model on which this project is based I would also like to thank my family and friends for their unconditional love and support I am truly grateful for the joy you bring to my life everyday and for the encouragement you have given me throughout this journey iii TABLE OF CONTENTS Chapter 1: Introduction 1.1 General 1.2 Objectives Chapter 2: Literature Review .3 2.1 Switchgrass 2.2 Ethanol Production 2.3 System Modeling Chapter 3: Model Development 3.1 Assumptions 3.2 Parameters 10 3.3 Procedure .13 Chapter 4: Results and Discussion 16 4.1 Simulation Results .16 4.2 Sensitivity Analysis .18 Chapter 5: Conclusion .21 References 23 Appendix A: Schematic Model 26 Appendix B: Switchgrass to Ethanol Library .27 Appendix C: Matlab Code 41 iv Chapter 1: Introduction 1.1 General This project investigates the impact of various parametric uncertainties on the overall material requirements for ethanol production, specifically feedstock requirements and land Through this study, insight can be gained to determine which processes have the greatest impact on uncertainty of outcomes Processes are examined beginning with harvesting switchgrass, proceeding through the conversion process, and addressing nutrient inputs and land area requirements to produce the desired amount of ethanol The primary processes that the harvested switchgrass undergoes are pretreatment, enzymatic hydrolysis, and fermentation Figure below summarizes the processes discussed in this report Figure Summary of processes for conversion of switchgrass to ethanol 1.2 Objectives The primary objective of this project is to estimate the impact of uncertainty on the material input requirements for production of a targeted level of ethanol The achievement of this objective requires the following steps Determine parameters with the most significant uncertainty in the process of producing ethanol from switchgrass Collect parameter data and characterize the nature of the uncertainty for each parameter determined in step 1, in the form of range and distribution type Develop a model for the ethanol production process and method to incorporate uncertainty into this model Conduct sensitivity analysis of uncertainty parameters on land area and harvest switchgrass Determine range of land area and harvested switchgrass based on desired amount of annual ethanol production Chapter 2: Literature Review 2.1 Switchgrass Switchgrass (Panicum virgatum) is a native North American warm season perennial grass, commonly cited as a potential dedicated bioenergy feedstock Switchgrass has emerged as a leading bioenergy feedstock due to this high-yielding, perennial grass’ broad cultivation range and low agronomic input requirements Switchgrass’ tolerance to heat, cold, and drought, as well as it’s resistance to pests and diseases, has enabled a variety of ecotypes of switchgrass to inhabit a wide range of climates and soil conditions throughout North America There are two general ecotypes of switchgrass: lowland and upland Lowland ecotypes are vigorous, tall, thick-stemmed and adaptable to wet conditions while the upland ecotypes are shorter, thinner-stemmed, and better suited to drier conditions (Gunter et al., 1996) Examples of lowland ecotypes are Alamo switchgrass; which is typically grown in the Deep South and midlatitudes, and Kanlow; an ecotype more tolerant of cold temperatures that is typically grown in mid-latitudes (Groode, 2008) Upland ecotypes include Cave-In-Rock, Blackwell, and Trailblazer, which are all recommended for central and northern states Switchgrass is typically harvested once in the fall or winter after a killing freeze After a freeze, nutrients travel into the plants root system This minimizes the harvest of plant nutrients, and the need to replace such nutrients, while also maximizing switchgrass yield Therefore, we assume a single, late-season harvest to make switchgrass production a sustainable low-input system (Larson et al., 2010) The assumed harvest period for switchgrass is between November and March (Larson et al., 2010) 2.2 Ethanol Production Although there will be only one harvest per year, once after senescence, a refinery will need a supply of feedstock throughout the year to produce ethanol This is achieved by using stored switchgrass during non-harvest periods Therefore, storage of switchgrass is a significant process in the switchgrass supply chain According to Larson et al., it is assumed that one-third of all harvested switchgrass is delivered to the biorefinery immediately after harvest in the harvest season, while the remaining two-thirds is stored and uniformly delivered to the plant during the non-harvest season, typically from March to October (2010) The U.S Department of Energy has identified switchgrass as a model herbaceous energy crop (Keshwani, 2009) Benefits of switchgrass include its high yield, low water and nutritional inputs, environmental benefits, and ability to thrive on marginal lands Because conventional farming equipment for seeding, crop management, and harvesting can be used, switchgrass can easily be integrating into existing farms (Keshwani, 2009) In fact, the Oak Ridge National Laboratory estimates that 171 million tons of switchgrass can be produced economically in the United States, on an annual basis (Bals et al., 2010) The main component in switchgrass is lignocellulose Lignocellulose is composed of cellulose, hemicellulose, and lignin, closely associated in a complex crystalline structure The conversion of lignocellulosic material to ethanol involves two main processes: hydrolysis of cellulose to fermentable reducing sugars and fermentation of the sugars to ethanol However, because the cellulose and hemicellulose are not readily available for enzymatic hydrolysis, an initial pretreatment step is required to increase accessibility of enzymes to the structural carbohydrate fraction Physical, chemical, and biological processes have all been used in biomass pretreatment Ammonia fiber explosion is a physiochemical method of pretreatment to solubilize and remove lignin and hemicellulose from the cellulose In the AFEX process, biomass is treated with liquid ammonia under high pressure (100 to 400 psi) and moderate temperatures (70 to 200°C) for less than 30 minutes (Bals et al 2010) The pressure is then rapidly released, exploding the fibrous mass This process decrystallizes the cellulose, hydrolyses the hemicellulose, removes and depolymerizes lignin, and increases the size of micropores on the cellulose surface (Bals et al 2010) This process results in treated biomass that can reach close to theoretical sugar yields due to increased susceptibility of lignocellulose to enzymatic hydrolysis Following pretreatment, the cellulose and hemicellulose can be enzymatically hydrolyzed, producing a mixture of fermentable sugars such as glucose and xylose Enzymatic hydrolysis proves to be an environmentally friendly alternative to using concentrated acid or alkaline reagents through the use of carbohydrate degrading enzymes, both cellulases and hemicellulases (Keshwani, 2009) Based on complete hydrolysis of the cellulose and hemicellulose to monomeric sugars, the maximum theoretical yield of reducing sugars is 800mg/g dry switchgrass (Dale et al 1996) Dale et al reports that the maximum rates and yields of sugar occur at AFEX conditions of 90 degrees Celsius, ammonia loading of gram per gram of biomass (ammonioa:biomass ratio of 1:1), and 15% moisture content These AFEX-treated samples yield to times more sugar compared with the untreated controls at the same enzyme loading The major advantage of SHF, compared to simultaneous saccharification and fermentation (SSF), is that it is possible to carry out the hydrolysis and fermentation at their own optimum conditions (Taherzadeh and Karimi, 2007) The resulting sugars can then be fermented to produce ethanol The fermented broth or mash is then further processed toward pure ethanol In order to assess the impact of various stages of this process, a method for modeling the overall system is required 2.3 System Modeling The aforementioned processes involved in producing ethanol from switchgrass are detailed in the input-output model in Appendix A In systems modeling, input-output models are used to represent interdependencies between stages of a system (Miller and Blair, 2009) Each node of an input-output model may contain several equations that together complete the system of equations that represents the whole model With this type of model, each process of ethanol production can be broken down by inputs and outputs Each output becomes the input for the subsequent process, demonstrating the interdependency between processes The figure below shows a snapshot of a small section of the switchgrass to ethanol inputoutput model The figure shows the detail in each individual process and how each process connects to the next Each of the processes shown in Figure represents a series of equations for that particular process Table below summarizes these equations for the processes in Figure Additionally, each process is connected at a node At each node the output from the previous process becomes the input for the subsequent process Table below summarizes the process connectivity for the processes by showing the nodal equations Figure Several processes of the switchgrass to ethanol input-output model Process 13 – Ethanol Recovery and Purification Flow Labels and Units Y0,13 = Concentrated Ethanol (95% mass), L/yr Y1,13 = Dilute Ethanol (12.5g/L or 1.25% mass ethanol), L/yr Y2,13 = Waste Water, L/yr Technology Coefficients References k1,13 = L Concentrated Ethanol = 0.00125 L Dilute Ethanol Seader, J.D and Ernest J Henley, 1990 Separation Process Principles k2,13 = L Waste Water = L Dilute Ethanol 1* * k2,13 = (800 l waste water/l conc eth)*(0.00125 l conc eth) = l waste water/l dilute eth 37 Process 14 – Ethanol Storage Flow Labels and Units Y1,14 = Ethanol influx, L/yr Y2,14 = Ethanol efflux, L/yr Technology Coefficients k2,14 = liter stored 95% ethanol per yr = liter 95% ethanol per yr References Assume no switchgrass is lost during this stage of storage 38 Process 16 – Switchgrass Boiler Flow Labels and Units Y0,16 Y1,16 Y2,16 Y3,16 Y4,16 = Spent (dry) switchgrass, mt/yr = Ash, mt/yr = H2O, mt/yr = Steam, mt/yr = Carbon Dioxide, mt/yr Technology Coefficients References k1,16 = Introduction to Chemical Engineering Thermodynamics, 6th ed., J.M Smith, H.C Van Ness and M.M Abbott, McGraw-Hill, 2001 k1,16 = k2,16 = k3,16 = k3,16 = k4,16 = mt ash = 0.05 [1-3] mt switchgrass mt ash = 0.273 [4] mt switchgrass mt H2O = 5.64 [1-3] mt switchgrass mt steam = 5.64 [1-3] mt switchgrass mt steam = 11.3 [4] mt switchgrass mt CO2 = 11.3 [1-3] mt switchgrass Elementary Principles of Chemical Processes, 3rd ed., R Felder and R Rousseau, J Wiley, 2000 McLaughlin, S., J Bouton, D Bransby, B Conger, W Ocumpaugh, D Parrish, C Taliaferro, K Vogel, and S Wullschleger 1999 Developing switchgrass as a bioenergy crop p 282-299 In: J Janick (ed.), Perspectives on new crops and new uses ASHS Press, Alexandria, VA Chang, S.V., W.E Kaar, B Burr, and M.T Holtzapple 2001 Simultaneous saccharification and fermentation of lime-treated biomass Biotechnology Letters, 23: 1327-1333 39 Process 17 – Steam Turbine & Generator Flow Labels and Units Y0,17 = Steam, mt/yr Y1,17 = Spent steam, mt/yr Technology Coefficients k1,17 = mt spent steam = mt steam References Assume steam entering this process is equal to spent steam 40 Appendix C: Matlab Code % % % % FILE NAME: Switch_ModelV5.m % % DATE: July 2011 % % NAME: Mariel Eisenberg % Department of Biological and Environmental Engineering % Cornell University % Ithaca, NY 14853 % % PURPOSE: Function in which the main parameters of interest % in Processes 1, 3, 10 and 11 are all varied to % calculate all material flows for switchgrass conversion % to ethanol % % REFERENCE: Masters of Engineering Project: "Assessing the Impact of % Uncertainty on Ethanol Production Outcomes" % function [y] = Switch_ModelV5(k1_1, k2_1, k3_1, k4_1, k5_1, k2_3, k1_10, k3_10, k4_10, k1_11, k2_11, k3_11, k4_11, k5_11, Y2_14) clc; % P2 - Switchgrass Grinding k1_2 = 9; %mt harvested SG/mt harvested SG % P3 - Switchgrass Storage %k2_3 = 1; %mt stored hammermilled SG/mt hamermilled SG % P6 - Swithgrass Storage k2_6 = 1; %mt stored delivered SG/mt delivered SG % P7 - Switchgrass Grinding k1_7 = 95; %mt reduced SG/mt delivered SG % P8 k1_8 = k2_8 = k3_8 = k4_8 = - Switchgrass Pretreatment 1; %mt pretreated SG/mt reduced BM 1000; %kg liquid ammonia/mt reduced BM 990; %l recycled ammonia/mt reduced BM 0.110; %mt H20/mt reduced BM % P9 k1_9 = k2_9 = k3_9 = - Cellulose Hydrolysis 1.0; %mt hydrolyzed BM/mt pretreated BM 5.0*10^6; %FPU enzymes/mt pretreated BM 2*10^4; %l buffer (citrate)/mt pretreated BM 41 k2_10 = 0.629; %mt spent solids/mt hydrolyzed BM % P12 - Cellulose Hydrolysis k1_12 = 1.0; %l dilute ethanol/l borth k2_12 = 003; %kg/l borth % P13 - Separation of single-cell protein from broth k1_13 = 0.00125; %l concentrated ethanol/l dilute ethanol k2_13 = 1.0; %l waste water/l dilute ethanol % P14 - Ethanol Storage k2_14 = 1; %l ethanol efflux/l ethanol influx % P16 k1_16 = k2_16 = k3_16 = k4_16 = - Switchgrass Boiler 05; %mt ash/mt switchgrass 5.64; %mt water/mt switchgrass 5.64; %mt steam/mt switchgrass 3.80; %mt carbon dioxide/mt switchgrass % P17 - Steam Condensation k1_17 = 1.0; %mt spent steam/mt steam % Retrieve Known Stimulus Variables % fprintf(' \r'); % Y2_14 = input('Enter Value for 95% Ethanol Produced liters/yr, Y2,14: '); % fprintf(' \r'); % % SET-UP A MATRIX % % Mapping of Solution Vector to Material Flows % Y0,1 Y1,1 Y2,1 Y3,1 Y4,1 Y5,1 Y0,2 Y1,2 Y1,3 Y2,3 Y0,4 Y1,6 Y2,6 Y0,7 Y1,7 Y0,8 Y1,8 Y2,8 Y3,8 Y4,8 Y0,9 Y1,9 Y2,9 Y3,9 Y0,10 Y1,10 Y2,10 Y3,10 Y4,10 Y0,11 Y1,11 Y2,11 Y3,11 Y4,11 Y5,11 Y0,12 Y1,12 Y2,12 Y0,13 Y1,13 Y2,13 Y1,14 Y0,16 Y1,16 Y2,16 Y3,16 Y4,16 Y0,17 Y1,17 Y1,20 Y1,21 % y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 y21 y22 y23 y24 y25 y26 y27 y28 y29 y30 y31 y32 y33 y34 y35 y36 y37 y38 y39 y40 y41 y42 y43 y44 y45 y46 y47 y48 y49 y50 y51 42 43 % % Check the size of the matrix % % % fprintf(' \r'); % fprintf(' \r'); % fprintf(' \r'); % fprintf('C Properties of A matrix: \r'); % fprintf(' \r'); % [n,m] = size(A); % fprintf(' \r'); % fprintf(' Number of row = %3.0f and Number of Column = %3.0f\r',n,m); % fprintf(' \r'); % % % % Check Whether the System of Equations has a Determinant % % % d=det(A); % fprintf(' The Determinant of A is %8.1f 1\r',d); % fprintf(' \r'); % fprintf(' \r'); % fprintf(' \r'); % fprintf(' Press any key to continue!\r'); % fprintf(' \r'); % pause; % % Set-up b vector % b= [0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; Y2_14; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0;]; % % Solve Linear System of Equations % y=A\b; Y0_1 = y(1); % Harvested Switchgrass mt/yr Y1_1 = y(2); % Nitrogen kg/yr Y2_1 = y(3); % Phosphate kg/yr Y3_1 = y(4); % Potassium kg/yr Y4_1 = y(5); % Pesticides kg/yr Y5_1 = y(6); % Land ha/yr Y0_2 = y(7); % Harvested Switchgrass mt/yr Y1_2 = y(8); % Hammer Milled Switchgrass mt/yr Y1_3 = y(9); % Hammer Milled Switchgrass mt/yr Y2_3 = y(10); % Switchgrass mt/yr Y0_4 = y(11); % Switchgrass transport to plan mt/yr Y1_6 = y(12); % Delivered Switchgrass mt/yr Y2_6 = y(13); % Delivered Switchgrass mt/yr Y0_7 = y(14); % Size Reduced Switchgrass mt/yr Y1_7 = y(15); % Delivered Switchgrass mt/yr Y0_8 = y(16); % Pretreated Biomass mt/yr Y1_8 = y(17); % Size Reduced mt/yr Y2_8 = y(18); % Liquid Ammonia l/yr Y3_8 = y(19); % Recycled Ammonia l/yr Y4_8 = y(20); % H20 mt/yr Y0_9 = y(21); % Hydrolyzed Biomass mt/yr Y1_9 = y(22); % Pretreated Biomass mt/yr Y2_9 = y(23); % Enzymes FPU/yr Y3_9 = y(24); % Citrate Buffer l/yr Y0_10 = y(25); % Sugar Solution mt/yr Y1_10 = y(26); % Hydrolyzed Biomass mt/yr Y2_10 = y(27); % Spent Solids mt/yr Y3_10 = y(28); % H20 l/yr 44 Y4_10 Y0_11 Y1_11 Y2_11 Y3_11 Y4_11 Y5_11 Y0_12 Y1_12 Y2_12 Y0_13 Y1_13 Y2_13 Y1_14 Y0_16 Y1_16 Y2_16 Y3_16 Y4_16 Y0_17 Y1_17 Y1_20 Y1_21 = = = = = = = = = = = = = = = = = = = = = = = y(29); y(30); y(31); y(32); y(33); y(34); y(35); y(36); y(37); y(38); y(39); y(40); y(41); y(42); y(43); y(44); y(45); y(46); y(47); y(48); y(49); y(50); y(51); % Excess H20 l/yr % Ferm Broth (3g/L cell mass & 12.5 g/l ethanol) l/yr % Sugar Solution (10g/L xylose & 20.0 g/l glucose) mt/yr % Bactopepton kg/yr % Yeast Extract kg/yr % Yeast kg/yr % CO2 kg/yr % Dilute Ethanol l/yr % Ferm Broth (3g/L cell mass & 12.5 g/l ethanol) l/yr % High Protein Bioprod kg/yr % 95 Ethanol l/yr % Dilute Ethanol l/yr %Waste Water l/yr %95 Ethanol l/yr %Spent Solids mt/yr %Ash mt/yr %H20 mt/yr %Steam mt/yr %CO2 mt/yr %Steam mt/yr %H20 mt/yr %Make-up Ammonia l/yr %Make-up H20 mt/yr % % 45 % % % FILE NAME: Vary_Parameters.m % % DATE: July 2011 % % NAME: Mariel Eisenberg % Department of Biological and Environmental Engineering % Cornell University % Ithaca, NY 14853 % % PURPOSE: To call function Switch_ModelV5 in which the main % parameters of interest in Processes 1, 3, 10 and 11 % are all varied to calculate all material flows for % switchgrass conversion to ethanol % % REFERENCE: Masters of Engineering Project: "Assessing the Impact of % Uncertainty on Ethanol Production Outcomes" % clear all; %Clear All clc; %Clear Command Window Display s = input('Enter the number of simulations, s: '); Y2_14 = input('Enter Value for 95% Ethanol Produced liters/yr, Y2,14: '); for n=1:s % P1 - Switchgrass Production % %Select Crop Yield from Normal Distribution ~N(12.5, 2.8) (mt Biomass/ha) k5_1(n) = (normrnd(12.5,2.8))^-1; %ha/mt Biomass %Fixed application rate of Nitrogen K1_1= 112; %kg N/ha k1_1(n)= (K1_1)*k5_1(n); %kg N/mt %Fixed application rate of Phosphate K2_1 = 50; % kg Phosphate/ha k2_1(n)= (K2_1)*k5_1(n); %kg P/mt %Fixed application rate of Potassium K3_1 = 112; % kg Potassium/ha k3_1(n)= (K3_1)*k5_1(n); %kg K/mt %Fixed application rate of Pesticides K4_1 = 1.75; % kg Pesticides/ha k4_1(n)= (K4_1)*k5_1(n); %kg Pesticides/mt % P3 - Switchgrass Storage K2_3(n) = unifrnd(0.1,0.25); %(continuous uniform distribution) %moisture content k2_3_max(n) = 0.3793*K2_3(n)+0.0368; %max dry matter loss t_stor= 200; %time in days k2_3(n)= 1-((k2_3_max(n))*(1-exp(-t_stor/180))); %mt stored hammermilled SG/mt hamermilled SG at time of storage 46 % P10 - Lignin recovery/filtration of sugar stream k1_10(n) = unifrnd(0.550,0.600); %(continuous uniform distribution) solution/mt hydrolyzed BM K3_10 = 4900; %l water/mt sugar solution k3_10(n) = (K3_10)*(k1_10(n)); %mt spent solids/mt hydrolyzed BM K4_10 = 7.258; %mt excess water/mt sugar solution k4_10(n) = (K4_10)*(k1_10(n)); %mt excess water/mt hydrolyzed BM mt sugar % P11 - Fermentation of sugar stream % K1_11(n) = unifrnd(0.9,0.94); %(continuous uniform distribution), theoretical yield = 0.51 g ethanol/g glucose) k1_11(n) = (((12.5)/(0.51*K1_11(n)))^-1)*(10^6); %liters Broth/ mt sugar solution K2_11 = 0.02; %kg Bactopepetone/liters Broth k2_11(n) = (K2_11)*(k1_11(n)); %kg Bactopepetone/mt sugar solution K3_11 = 0.01; %kg Yeast/liters Broth k3_11(n) = (K3_11)*(k1_11(n)); %kg Yeast/mt sugar solution K4_11 = 0.0006; %kg Yeast/liters Broth k4_11(n) = (K4_11)*(k1_11(n)); %kg Yeast/mt sugar solution K5_11 = 0.489; %kg CO2/kg sugar solution k5_11(n) = (K5_11)*(k1_11(n)); %kg CO2/mt sugar solution [y(:,n)] = Switch_ModelV5(k1_1(n), k2_1(n), k3_1(n), k4_1(n), k5_1(n), k2_3(n), k1_10(n), k3_10(n), k4_10(n),k1_11(n), k2_11(n), k3_11(n), k4_11(n), k5_11(n), Y2_14); M=[transpose(k5_1) transpose(k2_3) transpose(k1_10) transpose(k1_11) transpose(y(1,:))]; %creates matrix for linear regression end % % % % %Send Results to Excel Spreadsheet titled 'SGResults' Run=[1:s]; xlswrite('SGResults',Run, 'Results', 'D1') xlswrite('SGResults',y, 'Results', 'D2'); fprintf(' \r'); fprintf(' \r'); fprintf(' \r'); fprintf(' \r'); fprintf('RESULTS\r'); fprintf(' \r'); y5_1= y(6,:); %land area (ha) max(y5_1); min(y5_1); mean(y5_1); std(y5_1); fprintf('y(6) = Y5,1 - Average Land Area = %8.2f ha/yr \n',mean(y5_1)); fprintf('y(6) = Y5,1 - Standard Deviation Land Area = %8.2f ha/yr \n',std(y5_1)); y0_1= y(1,:); max(y0_1); min(y0_1); mean(y0_1); std(y0_1); %harvested switchgrass (mt/yr) 47 fprintf('y(1) = Y0,1 fprintf('y(1) = Y0,1 \n',std(y0_1)); fprintf('k5,1 fprintf('k5,1 - Average Harvested switchgrass = %8.2f mt/yr \n',mean(y0_1)); - Standard Deviation Harvested switchgrass = %8.2f mt/yr - Average Crop Yield = %8.2f ha/mt \n',mean(k5_1)); - Standard Deviation Crop Yield = %8.2f ha/mt \n',std(k5_1)); fprintf('k2,3 - Average Switchgrass Storage Loss = %8.2f mt/mt \n',mean(k2_3)); fprintf('k2,3 - Standard Deviation Switchgrass Storage Loss = %8.2f mt/mt \n',std(k2_3)); fprintf('k1,10 fprintf('k1,10 - Average Sugar Yield = %8.2f mt/mt \n',mean(k1_10)); - Standard Deviation Sugar Yield = %8.2f mt/mt \n',std(k1_10)); fprintf('k1,11 fprintf('k1,11 - Average Fermentation Yield = %8.2f l/mt \n',mean(k1_11)); - Standard Deviation Fermentation Yield = %8.2f l/mt \n',std(k1_11)); % %Dispay results from single simulation % fprintf(' _\r'); fprintf(' \r'); fprintf('RESULTS FROM SINGLE RUN\r'); fprintf(' \r'); fprintf('y(1) = Y0,1 - Harvested switchgrass = %8.1f mt/yr \n',y(1)); fprintf('y(2) = Y1,1 - Nitrogen = %8.1f kg/yr\n',y(2)); fprintf('y(3) = Y2,1 - P2O5 - Phosphate = %8.1f kg/yr\n',y(3)); fprintf('y(4) = Y3,1 - K2O - Potassium = %8.1f kg/yr\n',y(4)); fprintf('y(5) = Y4,1 - Pesticides = %8.1f kg/yr\n',y(5)); fprintf('y(6) = Y5,1 - Land = %8.1f ha/yr\n',y(6)); fprintf('y(7) = Y0,2 - Harvested switchgrass = %8.1f mt/yr\n',y(7)); fprintf('y(8) = Y1,2 - Hammer Milled switchgrass = %8.1f mt/yr\n',y(8)); fprintf('y(9) = Y1,3 - Hammer Milled switchgrass = %8.1f mt\n',y(9)); fprintf('y(10) = Y2,3 - Switchgrass =%8.1f mt/yr\n',y(10)); fprintf('y(11) = Y0,4 - Switchgrass transport to plant =%8.1f mt/yr\n',y(11)); fprintf('y(12) = Y1,6 - Delivered switchgrass = %8.1f mt/yr\n',y(12)); fprintf('y(13) = Y2,6 - Delivered switchgrass = %8.1f mt/yr\n',y(13)); fprintf('y(14) = Y0,7 - Size reduced switchgrass = %8.1f mt/yr\n',y(14)); fprintf('y(15) = Y1,7 - Delivered switchgrass = %8.1f mt/yr\n',y(15)); fprintf('y(16) = Y0,8 - Pretreated biomass = %8.1f mt/yr\n',y(16)); fprintf('y(17) = Y1,8 - Size reduced = %8.1f mt/yr\n',y(17)); fprintf('y(18) = Y2,8 - Liquid ammonia = %8.1f kg/yr\n',y(18)); fprintf('y(19) = Y3,8 - Recycled ammonia = %8.1f 1/yr\n',y(19)); fprintf('y(20) = Y4,8 - H2O = %8.1f mt/yr\n',y(20)); fprintf('y(21) = Y0,9 - Hydrolyzed biomass = %8.1f mt/yr\n',y(21)); fprintf('y(22) = Y1,9 - Pretreated biomass = %8.1f mt/yr\n',y(22)); fprintf('y(23) = Y2,9 - Enzymes = %8.3g FPU/yr\n',y(23)); fprintf('y(24) = Y3,9 - Citrate buffer = %8.1f l/yr\n',y(24)); fprintf('y(25) = Y0,10 - Sugar solution = %8.1f mt/yr\n',y(25)); fprintf('y(26) = Y1,10 - Hydrolyzed biomass = %8.1f mt/yr\n',y(26)); fprintf('y(27) = Y2,10 - Spent solids = %8.1f mt/yr\n',y(27)); fprintf('y(28) = Y3,10 - H20 = %8.1f l/yr\n',y(28)); fprintf('y(29) = Y4,10 - Excess H20 = %8.1f l/yr\n',y(29)); fprintf('y(30) = Y0,11 - Ferm broth (3g/L cell mass & 12.5 g/l ethanol) = %8.1f 1/yr\n',y(30)); fprintf('y(31) = Y1,11 - Sugar solution (10g/L xylose & 20.0 g/l glucose) = %8.1f mt/yr\n',y(31)); fprintf('y(32) = Y2,11 - Bactopeptone = %8.1f kg/yr\n',y(32)); fprintf('y(33) = Y3,11 - Yeast extract = %8.1f kg/yr\n',y(33)); fprintf('y(34) = Y4,11 - Yeast = %8.1f kg/yr\n',y(34)); fprintf('y(35) = Y5,11 - CO2 = %8.1f kg/yr\n',y(35)); fprintf('y(36) = Y0,12 - Dilute ethanol = %8.1f 1/yr\n',y(36)); 48 fprintf('y(37) = Y1,12 - Ferm broth (3g/L cell mass & 12.5 g/l ethanol) = %8.1f 1/yr\n',y(37)); fprintf('y(38) = Y2,12 - High Protein Bioprod = %8.1f kg/yr\n',y(38)); fprintf('y(39) = Y0,13 - 95 ethanol = %8.1f 1/yr\n',y(39)); fprintf('y(40) = Y1,13 - Dilute ethanol = %8.1f 1/yr\n',y(40)); fprintf('y(41) = Y2,13 - Waste Water = %8.1f 1/yr\n',y(41)); fprintf('y(42) = Y1,14 - 95 ethanol = %8.1f 1/yr\n',y(42)); fprintf('y(43) = Y0,16 - Spent solids = %8.1f mt/yr\n',y(43)); fprintf('y(44) = Y1,16 - Ash = %8.1f mt/yr\n',y(44)); fprintf('y(45) = Y2,16 - H2O = %8.1f mt/yr\n',y(45)); fprintf('y(46) = Y3,16 - Steam = %8.1f mt/yr\n',y(46)); fprintf('y(47) = Y4,16 - CO2 = %8.1f mt/yr\n',y(47)); fprintf('y(48) = Y0,17 - Steam = %8.1f mt/yr\n',y(48)); fprintf('y(49) = Y1,17 - H2O = %8.1f mt/yr\n',y(49)); fprintf('y(50) = Y1,20 - Make-up Ammonia = %8.1f l/yr\n',y(50)); fprintf('y(51) = Y1,21 - Make-up H2O = %8.1f mt/yr\n',y(51)); fprintf(' \r'); fprintf(' \r'); fprintf(' \r'); figure(1) hist(y(1,:)); title('Harvested Switchgrass Y_0_,_1 (mt/yr)'); % figure(2) % hist(y(2,:)); % title('Nitrogen Y_1_,_1 (kg/yr)'); % % figure(3) % hist(y(3,:)); % title('Phosphate Y_2_,_1 (kg/yr)'); % % figure(4) % hist(y(5,:)); % title('Potassium Y_3_,_1 (kg/yr)'); % % figure(5) % hist(y(5,:)); % title('Pesticides Y_4_,_1 (kg/yr)'); % figure(6) hist(y(6,:)); title('Land Area Y_5_,_1 (ha/yr)'); % % figure(7) % hist(k2_3); % title('Switchgrass Storage Loss k_2_,_3 (mt/mt)'); % % figure(7) % hist(y(8,:)); % title('Hammermilled Switchgrass Y_1_,_2 (mt/yr)'); % % figure(8) % hist(y(14,:)); % title('Reduced Switchgrass Y_0_,_7 (mt/yr)'); % % figure(9) % hist(y(21,:)); % title('Hydrolyzed Biomass Y_0_,_9 (mt/yr)'); % % figure(10) % hist(y(25,:)); 49 % % % % % title('Sugar Solution Y_0_,_10 (mt/yr)'); figure(11) hist(y(30,:)); title('Ferm Broth Y_0_,_11 (l/yr)'); 50 51 ... The conversion of lignocellulosic 21 feedstock to ethanol is an emerging technology and therefore there are many unknowns in the context of long-term ethanol production Hence, in the future, the. .. switchgrass conversion % to ethanol % % REFERENCE: Masters of Engineering Project: "Assessing the Impact of % Uncertainty on Ethanol Production Outcomes" % function [y] =... project is to estimate the impact of uncertainty on the material input requirements for production of a targeted level of ethanol The achievement of this objective requires the following steps Determine