# Development of the IBSAL-SimMOpt Method for the Optimization of Quality in a Corn Stover Supply Chain

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

_{2}emissions) in a biomass logistic system. Searcy and Hartley [20] utilize the IBSAL for evaluating a system of transportation and storage of herbaceous biomass in terms of volumetric percentages of different biomass quality classes.

^{®}™ to build a DES model for a typical biomass production chain. The performance measures that are considered in this model include transportation time, chipping time, and idle time among others.

## 3. Methodology

#### 3.1. The Impact of Feedstock Quality

#### 3.2. Interaction Between the Extended IBSAL and the SimMOpt Optimization Method

^{®}™.

- Extended IBSAL: This research is especially concerned with the costs derived from imperfect quality of feedstock. The IBSAL model is extended so that the cost of imperfect quality of the feedstock is estimated for the corn stover-to-bioethanol SC. For this purpose, blocks were added to the baseline IBSAL for estimating these quality-related costs and other performance measures of interest (e.g., dry matter loss at operations designed to improve the physical characteristics of the feedstock, percentage of moisture, percentage of ash, and probability of having non-conforming stover at the gate of the biorefinery). Extended IBSAL accurately represents the stochastic behavior of elements intervening in the SC and estimates the costs derived from collection, storage, and transportation operations and from having imperfect quality of feedstock.
- SimMOpt: The SimMOpt method is inspired on the procedure used by the stochastic Multi-Objective Simulated Annealing (MOSA). It can be expected that the solutions obtained with this method converge to global optima given the appropriate SA schedule. However, just as with any metaheuristic, optimality is not guaranteed. The procedure can be described as follows:
- (a)
- At the initial iteration, a large set of random solutions is computed. The best solutions for each objective of interest determine a cell in the grid of the Pareto front. The initial temperatures are computed.
- (b)
- Solutions are perturbed and several replications of the evaluation of the objectives are computed. This part is where the extended IBSAL connects with the SimMOpt. The extended IBSAL model is used to perform the replication and return the evaluations of the performance measures to the SimMOpt.
- (c)
- The SimMOpt method uses some hypothesis testing techniques and the Pareto Archive Evolution Strategy (PAES) with the evaluations of the performance measures from the extended IBSAL to find good solutions and perturb the solution that will be used in the following iteration.
- (d)
- The procedure is repeated until some conditions are met (e.g., minimum temperature, value of the objectives, maximum number of iterations, maximum number of rejects, and maximum number of accepts).

- The extended IBSAL and the SimMOpt method are coupled (extended IBSAL-SimMOpt) and used to minimize the set of competing objectives.
- Non-dominated solutions and Pareto-optimal fronts constructed from these solutions are obtained for multiple SA schedules for the problem in the case study.
- Performances of the Pareto-optimal fronts are evaluated using the hypervolume indicator approach.

#### 3.3. Detailed Description of the SimMOpt Optimization Method

#### 3.4. Extended IBSAL-SimMopt: Quality Related Blocks

^{2}): Khanchi and Birrel [39] evaluate the effect of rainfall and swath density on dry matter, and the composition change during the drying of corn stover. They assume three possible densities 0.8, 1.3, and 2.6 kg/m

^{2}(low density—LD, medium density—MD, and high density—HD, respectively). For this study, the windrow density is assumed to be uniformly distributed (0.8 and 2.6) (decimal). Refer to block (195, 15) in the Shred/Windrow Module—extended IBSAL.

_{1}= 6.4424 × 10

^{−5}, k

_{2}= 22.15, and k

_{3}= 0.4795 [7,25].

## 4. Case Study

#### 4.1. Geographical Regions and Farm Input Data

#### 4.2. Local Weather Input Data

^{2}).

#### 4.3. Machinery Input Data

#### 4.4. Storage and Other Operational/Quality Input Data

#### 4.5. IBSAL Set Up and Tuning

## 5. Results and Discussion

^{®}™ equipment, and finally to the truck. This process results in values of this attribute uniformly distributed within a small range about values that depends on the harvest day and the number of days that the stover is left on the field to dry. In this case study, the initial moisture content after harvest is uniformly distributed with values close to 73% (Equation (4)). In Figure 6, Figure 7 and Figure 8, it can be observed that this value is brought down by performing operations designed to improve the physical properties of feedstock to values in the range from 45% to 53%.

^{®}™ CORE

^{®}™ i7-3632QM central processing unit (CPU) processor that operates at 2.20 GHz with 8 GB of random access memory (RAM). The extended IBSAL model was modified and executed using Imagine That! Inc.

^{®}™ ExtendSim

^{®}™ 9 advanced technology (AT). The SimMOpt method was coded using Microsoft

^{®}™ (MS) Excel VBA

^{®}™.

## 6. Conclusions

- Provides a novel method for finding near-optimal solutions to the problem of designing biomass supply chains that improve the physical characteristics of the feedstock at an acceptable cost.
- Provides the bioenergy sector and the research community with a simulation-based optimization model with activity-based costing that is capable of representing the stochastic behavior of some elements intervening in the corn stover SC for the production of bioethanol. This analytical model can be used for decision making as well as educational and training purposes since it allows the user to conduct “what if” scenarios.
- Optimizes a set of competing individual objectives that include the cost of having imperfect corn stover quality for the production of bioethanol. The bioenergy industry will potentially benefit from this methodology, which can be adapted to minimize multiple costs in the biomass supply chain (e.g., collection, storage, transportation, and quality-related) as well as other key output measurements (e.g., dry matter loss, moisture content, and ash content) within a computational effort that is sensible for the solution of real problems. Here, the decision maker assigns the priority of objectives when finding a solution.
- Allows modeling complex systems with multiple intertwined stochastic elements; this is one of the most powerful features. The IBSAL-SimMOpt method captures the variability in the behavior of the elements of the biomass SC. Replications of the IBSAL model are performed to represent the stochastic behavior of the system.
- The SimMOpt method interacts with the output from the extended IBSAL model. There are no limitations in terms of the characteristics of the equalities/inequalities representing the objectives and constraints, which is an important concern when using mathematical programming instead of simulation.
- The IBSAL-SimMOpt method can be modified with relative ease using a top-down or bottom-up approach, as required. Therefore, the method can be easily transferred to applications involving biomass SCs with a wider range of feedstock to larger instances.

## Acknowledgments

^{®}™ by donating of a full version of ExtendSim

^{®}™ through the ExtendSim Research Grant is gratefully acknowledged. The research work on the IBSAL model by Sokhansanj, Turhollow, and Ebadian was relevant for the development of the proposed approach.

## Author Contributions

## Conflicts of Interest

## Appendix A

Symbols | |

S | Set of objective |

M | Set of solutions |

X_{m} | Solution m (m $\in $ M) |

E(X_{m})_{s} | Evaluation of objective s by considering solution m (s $\in $ S, m $\in $ M) |

T_{s} | Temperature related to objective s (s $\in $ S) |

N_{Bi} | The i-th return-to-base occurs after these many iterations |

N | Set of replications |

M_{init} | Set of initial solutions |

W | Set of computed solutions |

X_{m} | Solution m (m $\in $ M_{ini}) |

M_{i} = X_{m,s}* | Best initial solution for objective s (m $\in $ M_{init}, i $\in $ M_{init}, s $\in $ S) |

M_{j} = X_{m,s}* | Worst initial solution for objective s. (m $\in $ M_{init}, j $\in $ M_{init}, s $\in $ S) |

x_{w} | Solution w computed by a series of perturbations starting from M_{i} (w $\in $ W) |

E(X_{w})_{s} | Evaluations of objective s by considering solution X_{w} (s $\in $ S, w $\in $ W) |

T_{s_init} | Initial temperature |

T_{s} | Temperature |

Ē(X_{w})_{s} | Mean value from evaluations of objective s by considering solution X_{w} (s $\in $ S, w $\in $ W) |

StdDev(X_{w})_{s} | Standard deviation from evaluations of objective s by considering solution X_{w} (s $\in $ S, w $\in $ W) |

Runs_{max} | Maximum number of runs (SA schedule) |

Rejects_{max} | Maximum number of rejects (SA schedule) |

Accepts_{max} | Maximum number of accepts (SA schedule) |

z_{s} | Critical value (Hypotheses testing on the mean of evaluations of objective s for two large samples) (s $\in $ S) |

n_{w} | Size of sample consisting of the evaluations of objectives while considering solution X_{w} (w $\in $ W) |

z_{α} | Value from standard normal table corresponding to significance level α. |

R | Pseudo-random number ~Uniform(0, 1) |

P_{s} | Probability of acceptance of solution X_{w} based on the mean of the evaluations of objective s (s $\in $ S) |

K | Boltzmann constant |

Sched. | SA Schedule |

Pr | Probability of acceptance of neighbor solution |

MinTemp | Minimum Temperature—stopping criterion |

MinOFVal | Minimum value of the objective evaluation—stopping criterion |

A_{li} | Number of solutions in the candidate list from which a solution is selected when the SimMOpt returns-to-base |

Lm | Zero dry matter loss (due to machine) at 80% moisture content |

Lmmax | Machine biomass loss (decimal) |

MoistContWet | Moisture content in stover (decimal) |

Mwmax | Moisture content with zero machine loss (decimal wb) |

DMLoss | Loss tonnage due to machine (tonne) |

ItemTom | Tonnage of item (e.g., land) |

MoistGrainInit | Initial moisture content of the grain (decimal) |

WindrowDensity | Windrow density (kg/m^{2}) |

MoistEq(t) | Equilibrium moisture content on the grain (cob) at time t (decimal) |

RH(t) | relative humidity at time t (decimal) |

Temp(t) | Ambient air temperature at time t (°C) |

P(t) | Cumulative harvested fraction at time t (decimal) |

CumHarvestAreaDay | Cumulative harvested area (considered for all farm lands harvested on the same day) (ac) |

MoistGrainWet(t) | Grain kernel moisture content at time t (decimal). Assumed ≥ 0.08 |

MoistContWet | Moisture content of the stover stalk (w.b.) (decimal) |

Mo | Initial moisture content (decimal) |

Me | Equilibrium moisture calculated by modified Oswin equation (decimal) |

K | Drying rate constant (applies to Equations (8) and (9)) |

AvgRH(t) | Average relative humidity at time t (decimal) |

VPD(t) | Vapor pressure density at time t |

Rad(t) | Radiation intensity for the hour at time t (watt/m^{2}) |

WS(t) | Wind speed at time t (m/s) |

T | Each day the stover is left on the field for dying (days) |

StartDayHarvest | Start day of harvest (type of decision variable) |

DaysInField | Number of days left on the field for drying (type of decision variable) |

AshCont | Ash content after field drying (decimal) |

Rainfall(t) | Amount of rain at time t (mm) |

DMLOSS | Dry matter loss during field drying (decimal) |

WrappingBales | Binary: wrapping/no wrapping (type of decision variable) |

DMLOSSWrap | Dry matter loss percentage for wrapped bales (decimal) |

CostQual | Cost of quality-related operations ($) |

AirClassification | Binary: classification/no classification |

CostNoQual | Dockage cost ($) |

Abbreviations | |

IBSAL | Integrated Biomass Supply Analysis and Logistics |

SimMOpt | Simulation-based Multi-Objective Optimization |

SA | Simulated Annealing |

NREL | National Renewable Energy Laboratory |

DES | Discrete Events Simulation |

SC | Supply Chain |

ORNL | Oak Ridge National Laboratory |

DoE | Department of Energy |

MP | Mathematical Programming |

QP | Quadratic Programming |

PROMETHEE | Preference Ranking Organization Method for Enrichment of Evaluations |

MILP | Mixed-Integer Linear Programming |

BLM | Biomass Logistic Model |

FS | Feedstock Supply |

IBSAL-MC | IBSAL Multi-Crop |

SD | Systems Dynamics |

MOSA | Multi-Objective Simulated Annealing |

PAES | Pareto Archive Evolution Strategy |

CTL | Central Limit Theorem |

w.b. | Wet Basis |

## Appendix B

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**Figure 4.**Geographical location (based on [44]).

**Figure 9.**Pareto fronts (schedule (*)): (

**a**) mean DMLOSS vs. mean cost; (

**b**) mean DMLOSS vs. mean percent moisture; (

**c**) mean DMLOSS vs. mean percent ash; (

**d**) mean DMLOSS vs. mean dockage cost; (

**e**) mean cost vs. mean percent ash; (

**f**) mean cost vs. mean percent moisture; (

**g**) mean cost vs. mean dockage cost; (

**h**) mean percent moisture vs. mean percent Ash; and (

**i**) mean dockage cost vs. mean percent moisture.

Title | Author | Objective/Feedstock | Method | IBSAL |
---|---|---|---|---|

Biomass Supply Chains | ||||

Development of a multicriteria assessment model for ranking biomass feedstock collection and transportation systems | Kumar et al. [9] | Rank alternatives for the collection and transportation of biomass. Feedstock: Biomass (corn stover). | Multicriteria assessment methodology (PROMETHEE I, II) and IBSAL | √ |

Switchgrass (Panicum vigratum, L.) delivery to a biorefinery using integrated biomass supply analysis and logistics (IBSAL) model | Kumar and Sokhansanj [10] | Evaluate a collection systems of switchgrass and analysis of the related costs, energy input, and carbon emissions. Feedstock: Switchgrass | Discrete event simulation | √ |

Cotton logistics as a model for a biomass transportation system | Ravula et al. [11] | Determine the operating parameters for reducing the transportation cost of biomass. Feedstock: Cotton gin (analogously to biomass). | Discrete event simulation | |

The impact of agricultural residue yield range on the delivered cost to a biorefinery in the Peace River region of Alberta, Canada | Stephen et al. [12] | Evaluate the impact of residue yield on the biomass delivered cost. Feedstock: Straw and chaff from wheat, barley, and oats. | Discrete event simulation | √ |

Techno-economic analysis of using corn stover to supply heat and power to a corn ethanol plant–Part 1: Cost of feedstock supply logistics | Sokhansanj et al. [13] | Propose a supply chain for corn stover to produce heat and power for a dry mill ethanol plant. Feedstock: Corn stover. | Discrete event simulation | √ |

A mathematical model to design a lignocellulosic biofuel supply chain system with a case study based on a region in Central Texas | An et al. [14] | Maximize the profit of a lignocellulosic biofuel supply chain. Feedstock: Switchgrass. | Multi-commodity flow mathematical model | |

Impact of distributed storage and pre-processing on Miscanthus production and provision systems | Shastri et al. [15] | Analyze the cost reduction from implementing distributed storage and pre-processing at satellite storage locations. Feedstock: Miscanthus. | Mixed integer linear programming (MILP) (BioFeed) optimization model | |

Economic and energy evaluation of a logistics system based on biomass modules | An and Searcy [16] | Minimize the feedstock costs in a logistic system by maximizing highway load and minimizing load/unload times. Feedstock: Cotton (analogously to biomass) | Discrete event simulation | √ |

An analysis of logistic costs to determine optimal size of a biofuel refinery | Larasati et al. [17] | Analyze the impact of logistic costs on the size of cellulosic ethanol biorefineries. Feedstock: Switchgrass. | Variable distance on grids approach | |

Development of an integrated tactical and operational planning model for supply of feedstock to a commercial-scale bioethanol plant | Ebadian et al. [18] | Integrate the tactical and operational levels in the biomass supply chain for a commercial-scale cellulosic ethanol plant. Feedstock: Multi-biomass. | Discrete event simulation/Mixed-integer linear programming | |

Biomass round bales infield aggregation logistics scenarios | Igathinathane, et al. [19] | Evaluate logistic scenarios for aggregating biomass bales to a field-edge stack or a storage. Feedstock: Biomass. | Computer simulation | |

Evaluation of a modular system for low-cost transport and storage of herbaceous biomass | Searcy and Hartley [20] | Optimize the harvest of energy sorghum in the humid southern region by using a modified cotton module builder for the formation of modules. Feedstock: Energy sorghum. | Discrete event simulation | √ |

Analyzing and comparing biomass feedstock supply systems in China: corn stover and sweet sorghum case studies | Ren et al. [21] | Analyze the harvest, collection, storage, transportation, preprocessing, handling, and queuing operations in the rural China biomass supply system. Feedstock: Corn stover and sweet sorghum. | System’s dynamics. Biomass Logistic Model (BLM) framework (BLM Sino-Feedstock Supply (FS)) | |

Simulation-based multi-objective model for supply chains with disruptions in transportation | Chavez et al. [22] | Optimize the supply of agricultural products by using a simulation-based optimization model. Feedstock: Agricultural products. | Simulation-based Optimization | |

Modelling a biomass supply chain through discrete-event simulation | Pinho et al. [23] | Providing a decisions tool for the biomass supply chain management services. Feedstock: Wood chip supply to a co-generation power plant. | Discrete event simulation (SIMEVENTS) | |

Quantifying the impact of feedstock quality on the design of bioenergy supply chain networks | Castillo-Villar, Minor-Popocatl, and Webb [24] | Present a mixed-integer quadratically constrained programming model that minimizes the bioethanol SC, considering quality related costs. Feedstock: Logging residues. | Mixed-integer quadratic programming | |

Development of the IBSAL model | ||||

Development and implementation of integrated biomass supply analysis and logistics model (IBSAL) | Sokhansanj et al. [7] | Describe the development of the IBSAL model. Feedstock: Corn stover. | Discrete event simulation | √ |

Development of the Integrated Biomass Supply Analysis and Logistics Model (IBSAL) | Sokhansanj, Turhollow, and Wilkerson [25] | Detailed description of the IBSAL model. Feedstock: Corn stover. | Discrete event simulation | √ |

A new simulation model for multi-agricultural biomass logistics system in bioenergy production | Ebadian et al. [26] | Propose a model for the supply of a mixture of feedstock to a cellulosic ethanol plant. Feedstock: Multi-agricultural. | Discrete event simulation (IBSAL-MC (multi-crop)) | √ |

Methods to optimise the design and management of biomass-for-bioenergy supply chains: A review | Meyer et al. [27] | Present an overview of the optimization methods and model focusing on the design of biomass SCs. Feedstock: Biomass. | Various methods | |

The ExtendSim Optimizer | Diamond [28] | Describe the technique used by the ExtendSim Optimizer^{®}™ to find the best set of parameters for a simulation model. Feedstock: N/A. | Simulation optimization | |

Availability of feedstock and technologies | ||||

Availability of corn stover as a sustainable feedstock for bioethanol production | Kadam and McMillan [29] | Analyze the potential long-term amount of corn stover available to ethanol plants. Feedstock: Corn stover. | Analysis of a generalized model for stover production and removal | |

Large-scale production, harvest and logistics of switchgrass (Panicum virgatum L.)—Current technology and envisioning a mature technology | Sokhansanj et al. [30] | Evaluation of technologies for the production, harvest, storage, and transportation. Feedstock: Switchgrass. | Empirical | |

Evaluation of a modular system for low-cost transport and storage of herbaceous biomass | Searcy and Hartley [20] | Optimize the harvest of energy sorghum in the humid southern region by using a modified cotton module builder for the formation of modules. Feedstock: Energy sorghum. | Discrete event simulation | √ |

Influence of weather on the predicted moisture content of field chopped energysorghum and switchgrass | Popp et al. [31] | Determine the effect of weather on harvested moisture content. Feedstock: Switchgrass and energy sorghum. | Discrete event simulation | √ |

Equipment | Input data |
---|---|

Mower-Conditioner | Width (ft), field speed average (mph), field efficiency (dec. fraction), machine efficiency, horsepower (hp), custom cost ($/h), annual fixed cost ($), variable cost ($/h), labor cost ($/h), purchase price ($), number of equipment, number of operators, machine biomass loss (decimal), and moisture content with zero machine loss (decimal w.b.) |

Windrow-Tractor | Power, (hp), total cost ($/h), annual fixed cost ($), variable cost ($/h), labor cost ($/h), and purchase price. |

Rectangular Baler | Baling pick up width (ft), field average speed (mph), field efficiency (dec. fraction), machine efficiency, power requirement (hp), custom cost ($/h), annual fixed cost ($), variable cost ($/h), labor cost ($/h), purchase price ($), number of equipment, number of operator, machine biomass loss (decimal), moisture content with zero machine loss (decimal w.b.), bale dimension width ( ft), bale dimension height (ft), bale dimension length (ft), bale density (lb/ft^{3}), bales mass (ton), and cost of twine per bale ($/bale). |

Tractor-Baler | Power (hp), total cost ($/h), annual fixed cost ($), variable cost ($/h), labor cost ($/h), and purchase price (S) |

Infield-Transporter | Bale size 3x4x8 (volume ft^{3}), bale density (lb/ft^{3}), number of bale per load, load tie time—securing load (min/bale), load efficiency, travel speed full (mph), travel speed empty (mph), efficiency travel, unloading weight and inspection time (min/bale), efficiency unload, power (hp), custom cost ($/h), annual fixed cost ($), variable cost ($/h), labor cost ($/h), purchase price ($), number of equipment, number of operators, machine biomass loss (decimal), moisture content with zero machine loss (decimal w.b.), and winding factor. |

Loader Storage | Number of bales per load, weight of each bale (ton), loading time per load (min), unloading time per load (min), efficiency, horsepower (hp), custom cost ($/h), annual fixed cost ($), operating cost ($/h), labor cost ($/h), purchase price ($), number of equipment, number of operators, machine biomass loss (decimal), and moisture content with zero machine loss (decimal w.b.) |

Flatbed Trailer | Bale size 3x4x8 (volume ft^{3}), bale density (lb/ft^{3}), number of bale per load, load tie time—securing load (min/bale), load efficiency, travel speed full (mph), travel speed empty (mph), efficiency travel, unloading weight and inspection time (min/bale), efficiency unload, power (hp), custom cost ($/h), annual fixed cost ($), variable cost ($/h), labor cost ($/h), purchase price ($), number of equipment, number of operators, machine biomass loss (decimal), moisture content with zero machine loss (decimal w.b.), and winding factor. |

Truck-Tractor | Power (hp), total cost ($/h), annual fixed cost ($), variable cost ($/h), labor cost ($/h), and purchase price |

Bale Wrapper | Purchase price, stone pad cost ($/m^{2}), cost of plastic wrap per bale, wrapping time (min/bale) |

Tarp | Tarp cost ($/ft^{2}), labor cost to remove and place tarps ($/ft^{2}), and tarp useful life (years) |

Sched. | Pr | MinTemp (all) | MinOFVal (all) | Rejects_{max} | Runs_{max} | Accepts_{max} | k | A_{li} | N_{Bi} |
---|---|---|---|---|---|---|---|---|---|

* | 0.95 | 0.001 | 0.01 | 10 | 10 | 10 | 1 | 10 | 100 |

** | 0.6 | 0.001 | 0.01 | 5 | 5 | 5 | 1 | 10 | 10 |

*** | 0.6 | 0.001 | 0.01 | 10 | 10 | 10 | 1 | 10 | 100 |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Chavez, H.; Castillo-Villar, K.K.; Webb, E.
Development of the IBSAL-SimMOpt Method for the Optimization of Quality in a Corn Stover Supply Chain. *Energies* **2017**, *10*, 1137.
https://doi.org/10.3390/en10081137

**AMA Style**

Chavez H, Castillo-Villar KK, Webb E.
Development of the IBSAL-SimMOpt Method for the Optimization of Quality in a Corn Stover Supply Chain. *Energies*. 2017; 10(8):1137.
https://doi.org/10.3390/en10081137

**Chicago/Turabian Style**

Chavez, Hernan, Krystel K. Castillo-Villar, and Erin Webb.
2017. "Development of the IBSAL-SimMOpt Method for the Optimization of Quality in a Corn Stover Supply Chain" *Energies* 10, no. 8: 1137.
https://doi.org/10.3390/en10081137