Kenneth M. Bryden

Modeling thermally thick pyrolysis of wood

Abstract A general model of the pyrolysis of a wood slab is presented and validated with a set of heat release data. The model is applied to particle half-thicknesses from 5 μm to 5 cm , temperatures from 800 to 2000 K , and moisture contents from 0% to 30%. Internal temperatures, pyrolysis rates and yields of tar, hydrocarbons and char are presented. Four pyrolysis regimes are identified, depending on external temperature and particle size: thermally thin—kinetically limited, thermally thin—heat transfer limited, thermally thick, and thermal wave regimes.

Modeling the combined impact of moisture and char shrinkage on the pyrolysis of a biomass particle

A detailed computational model of pyrolysis of a moist, shrinking biomass particle is presented. This model is used to examine the effect of varying the moisture content for a single shrinking biomass particle subjected to a constant external temperature. Particle half-thicknesses ranging from 5 μm to 2 cm, temperatures from 800 to 2000 K, moisture contents from 0 to 30% (dry basis), and shrinkage factors from 1.0 to 0.4 are examined. The impact of moisture content and shrinkage was found to be a function of pyrolysis regime. In general, coupling between moisture content and shrinkage was found to result in longer pyrolysis times than if they were considered separately. Additionally, coupling between moisture content and shrinkage increased tar yield and decreased light hydrocarbon yield compared to considering moisture and shrinkage separately.

Modeling the impact of shrinkage on the pyrolysis of dry biomass

A new method for modeling shrinkage of a biomass particle is presented and validated in a detailed wood pyrolysis model. This model is applied to particle half-thicknesses ranging from to , temperatures from 800 to , and shrinkage factors of 1.0–0.4. Internal temperatures, pyrolysis rates, and yields of tar, light hydrocarbons and char are presented. Based on the results of the model, it is found that shrinkage has a negligible affect on pyrolysis in the thermally thin (Bi 10) shrinkage affects both the pyrolysis time and the pyrolysis products. Char shrinkage impacts the pyrolysis process in several ways. These include thinning the pyrolysis reaction region and increasing the pyrolysis temperatures, reducing the residence time of the gases within the particle, and cooling the char layer due to the higher mass flux rates of pyrolysis products. Within the thermal wave pyrolysis regime these effects significantly reduce the light hydrocarbon yield, increase the tar yield, and reduce the pyrolysis time. In the case of a large 2-cm half-thickness poplar particle, with a background temperature of and a shrinkage factor of 0.4, inclusion of shrinkage reduces the pyrolysis time by 43%, increases the tar yield from 7% to 13%, decreases the light hydrocarbon yield from 66% to 60%, and decreases the char yield slightly.

Graph-based evolutionary algorithms

Evolutionary algorithms use crossover to combine information from pairs of solutions and use selection to retain the best solutions. Ideally, crossover takes distinct good features from each of the two structures involved. This process creates a conflict: progress results from crossing over structures with different features, but crossover produces new structures that are like their parents and so reduces the diversity on which it depends. As evolution continues, the algorithm searches a smaller and smaller portion of the search space. Mutation can help maintain diversity but is not a panacea for diversity loss. This paper explores evolutionary algorithms that use combinatorial graphs to limit possible crossover partners. These graphs limit the speed and manner in which information can spread giving competing solutions time to mature. This use of graphs is a computationally inexpensive method of picking a global level of tradeoff between exploration and exploitation. The results of using 26 graphs with a diverse collection of graphical properties are presented. The test problems used are: one-max, the De Jong functions, the Griewangk function in three to seven dimensions, the self-avoiding random walk problem in 9, 12, 16, 20, 25, 30, and 36 dimensions, the plus-one-recall-store (PORS) problem with n=15,16, and 17, location of length-six one-error-correcting DNA barcodes, and solving a simple differential equation semi-symbolically. The choice of combinatorial graph has a significant effect on the time-to-solution. In the cases studied, the optimal choice of graph improved solution time as much as 63-fold with typical impact being in the range of 15% to 100% variation. The graph yielding superior performance is found to be problem dependent. In general, the optimal graph diameter increases and the optimal average degree decreases with the complexity and difficulty of the fitness landscape. The use of diverse graphs as population structures for a collection of problems also permits a classification of the problems. A phylogenetic analysis of the problems using normalized time to solution on each graph groups the numerical problems as a clade together with one-max; self-avoiding walks form a clade with the semisymbolic differential equation solution; and the PORS and DNA barcode problems form a superclade with the numerical problems but are substantially distinct from them. This novel form of analysis has the potential to aid researchers choosing problems for a test suite

Numerical modeling of a deep, fixed bed combustor

A computational model to evaluate the anticipated performance characteristics of a deep, fixed bed combustor/gasifier utilizing whole trees as the source of fuel is presented. This combustor/ gasifier is the heat source for a proposed steam-driven electric power plant utilizing whole trees as the source of fuel. In the simulation model presented, hardwood logs 20 cm in diameter are burned in a 3.7 m deep fuel bed. Solid and gas velocity and CO, CO2, H2O, hydrocarbon, and O2 profiles are calculated. This deep bed combustor obtains high energy release rates per unit area due to the high inlet air velocity and extended reaction zone. The lowest portion of the overall bed is an oxidizing region and the remainder of the bed acts as a gasification and drying region. The overfire air region completes the combustion. Approximately 40% of the energy is released in the lower oxidizing region. The wood consumption rate obtained from the computational model is compared with test results obtained from full scale testing. The wood consumption rate predicted by the model is 2630 kg/(m2 h) which matches well the consumption rate of 2670 kg/ (m2 h) observed during the 2 h test period of the field test. This corresponds to a heat release rate of 9.6 MW/m2. The model is used to investigate the performance of the combustor under a variety of load conditions, fuel sizes, and moisture conditions.

An integrated model for assessment of sustainable agricultural residue removal limits for bioenergy systems

Agricultural residues have been identified as a significant potential resource for bioenergy production, but serious questions remain about the sustainability of harvesting residues. Agricultural residues play an important role in limiting soil erosion from wind and water and in maintaining soil organic carbon. Because of this, multiple factors must be considered when assessing sustainable residue harvest limits. Validated and accepted modeling tools for assessing these impacts include the Revised Universal Soil Loss Equation Version 2 (RUSLE2), the Wind Erosion Prediction System (WEPS), and the Soil Conditioning Index. Currently, these models do not work together as a single integrated model. Rather, use of these models requires manual interaction and data transfer. As a result, it is currently not feasible to use these computational tools to perform detailed sustainable agricultural residue availability assessments across large spatial domains or to consider a broad range of land management practices. This paper presents an integrated modeling strategy that couples existing datasets with the RUSLE2 water erosion, WEPS wind erosion, and Soil Conditioning Index soil carbon modeling tools to create a single integrated residue removal modeling system. This enables the exploration of the detailed sustainable residue harvest scenarios needed to establish sustainable residue availability. Using this computational tool, an assessment study of residue availability for the state of Iowa was performed. This study included all soil types in the state of Iowa, four representative crop rotation schemes, variable crop yields, three tillage management methods, and five residue removal methods. The key conclusions of this study are that under current management practices and crop yields nearly 26.5 million Mg of agricultural residue are sustainably accessible in the state of Iowa, and that through the adoption of no till practices residue removal could sustainably approach 40 million Mg. However, when considering the economics and logistics of residue harvest, yields below 2.25 Mg ha^-^1 are generally considered to not be viable for a commercial bioenergy system. Applying this constraint, the total agricultural residue resource available in Iowa under current management practices is 19 million Mg. Previously published results have shown residue availability from 22 million Mg to over 50 million Mg in Iowa.

Mixture Preparation Effects on Distributed Combustion for Gas Turbine Applications

Distributed Combustion is now known to provide significant improvements to the performance of gas turbine combustors. Key features of distributed combustion include uniform thermal field in the entire combustion chamber for significantly improved pattern factor and avoidance of hot-spot regions that promote thermal NO x emissions, negligible emissions of hydrocarbons and soot, low noise, and reduced air cooling requirements for turbine blades. Distributed combustion necessitates controlled mixing between the injected air, fuel, and hot reactive gases from within the combustor prior to mixture ignition. The mixing process impacts spontaneous ignition of the mixture to result in improved distributed combustion reactions. Distributed combustion can be achieved in premixed, partially premixed, or non-premixed modes of combustor operation with sufficient entrainment of hot and active species present in the combustion zone and their rapid turbulent mixing with the reactants. Distributed combustion with swirl is investigated here to further explore the beneficial aspects of such combustion under relevant gas turbine combustion conditions. The near-term goal is to develop a high-intensity combustor with ultra-low emissions of NOx and CO and a much improved pattern factor and eventual goal of near-zero emission combustor. Different fuel injection scenarios are examined with focus on mixing to achieve distributed reaction conditions and ultra-low emissions. In all the cases, air was injected tangentially to impart swirl to the flow inside the combustor. Ultra-low NO x emissions were found for both the premixed and non-premixed combustion modes for the geometries investigated here. Results showed very low levels of NO (~10 PPM) and CO (~21 PPM) emissions under non-premixed mode of combustion with air preheats at an equivalence ratio of 0.6 and a moderate heat release intensity of 27 MW/m3-atm. Further enhancement of the mixing process using dilution reduced NO emission to 4.6 PPM which is nearly equivalent to emissions under premixed combustion mode with reduced CO emissions compared to non-premixed combustion mode. Results are also reported on lean stability limits and OH* chemiluminescence under different fuel injection scenarios for determining the extent of distribution combustion conditions. Numerical simulations have also been performed to help develop an understanding of the mixing process for better understanding of ignition and combustion.

Optimization of heat transfer utilizing graph based evolutionary algorithms

Abstract This paper examines the use of graph based evolutionary algorithms (GBEAs) for optimization of heat transfer in a complex system. The specific case examined in this paper is the optimization of heat transfer in a biomass cookstove utilizing three-dimensional computational fluid dynamics to generate the fitness function. In this stove hot combustion gases are used to heat a cooking surface. The goal is to provide an even spatial temperature distribution on the cooking surface by redirecting the flow of combustion gases with baffles. The variables in the optimization are the position and size of the baffles, which are described by integer values. GBEAs are a novel type of EA in which a topology or geography is imposed on an evolving population of solutions. The choice of graph controls the rate at which solutions can spread within the population, impacting the diversity of solutions and convergence rate of the EAs. In this study, the choice of graph in the GBEAs changes the number of mating events required for convergence by a factor of approximately 2.25 and the diversity of the population by a factor of 2. These results confirm that by tuning the graph and parameters in GBEAs, computational time can be significantly reduced.

Risk-Based Analysis of the Danish Pork Salmonella Program : Past and Future

The Danish pork Salmonella control program was initiated in 1993 in response to a prominent pork-related outbreak in Copenhagen. It involved improved efforts at slaughter hygiene (postharvest) and on-farm (preharvest) surveillance and control. After 10 years, 95 million Euros, significant reductions in seropositive herds, Salmonella positive carcasses, and pork-attributable human cases (PAHC), questions have arisen about how best to continue this program. The objective of this study was to provide some analysis and information to address these questions. The methods used include a computer simulation model constructed of a series of Excel workbooks, one for each simulated year and scenario (http://www.ifss.iastate/DanSalmRisk). Each workbook has three modules representing the key processes affecting risk: seropositive pigs leaving the farm (Production), carcass contamination after slaughter (Slaughter), and PAHC of Salmonella (Attribution). Parameter estimates are derived from an extensive farm-to-fork database collected by industry and government and managed by the Danish Zoonosis Centre (http://www.food.dtu.dk). Retrospective (1994-2003) and prospective (2004-2013) simulations were evaluated. The retrospective simulations showed that, except for the first few years (1994-1998), the on-farm program had minimal impact in reducing the number of positive carcasses and PAHC. Most of the reductions in PAHC up to 2003 were, according to this analysis, due to various improvements in abattoir processes. Prospective simulations showed that minimal reductions in human health risk (PAHC) could be achieved with on-farm programs alone. Carcass decontamination was shown as the most effective means of reducing human risk, reducing PAHC to about 10% of the simulated 2004 level.

MEMS Fabry-Perot sensor interrogated by optical system-on-a-chip for simultaneous pressure and temperature sensing

We present a micro-electro-mechanical systems (MEMS) based Fabry-Perot (FP) sensor along with an optical system-on-a-chip (SOC) interrogator for simultaneous pressure and temperature sensing. The sensor employs a simple structure with an air-backed silicon membrane cross-axially bonded to a 45° polished optical fiber. This structure renders two cascaded FP cavities, enabling simultaneous pressure and temperature sensing in close proximity along the optical axis. The optical SOC consists of a broadband source, a MEMS FP tunable filter, a photodetector, and the supporting circuitry, serving as a miniature spectrometer for retrieving the two FP cavity lengths. Within the measured pressure and temperature ranges, experimental results demonstrate that the sensor exhibits a good linear response to external pressure and temperature changes.