A Simulation Model for a Hardwood Sawmill Decision Support System

The paper describes a sawmill simulation model developed as a component of an integrated decision support system for hardwood sawmills. Discussions focus primarily on some of the essential features of the simulator and how it can be used as a tool for designing sawmill facilities and in the evaluation of sawing policies and production plans. Further discussed are some of the discrete-event simulation modeling techniques used in developing the simulator

Development of a 3D log processing optimization system for small-scale sawmills to maximize profits and yields from central appalachian hardwoods

The current status of log sawing practices in small hardwood sawmills across West Virginia was investigated and the effects of log sawing practices on lumber recovery evaluated. A total of 230 logs two species, red oak (Quercus rubra) and yellow-poplar (Liriodendron tulipifera), were measured in five typical hardwood sawmills in the state. Log characteristics such as length, diameter, sweep, taper, and ellipticality were measured. Additionally, the characteristics of sawing equipment such as headrig type, headrig kerf width, and sawing thickness variation were recorded. A general linear model (GLM) was developed using Statistical Analysis System (SAS) to analyze the relationship between lumber recovery and the characteristics of logs and sawing equipment for small sawmills in West Virginia. The results showed that the factors of log grade, log diameter, species, log sweep, log length, different sawmills, the interaction between log species and grade, and the interaction between log species and log length had significant impacts on volume recovery. Log grade, log species and headrig type had significant effects on value recovery.;Hardwood lumber production includes a sequence of interrelated operations. Methods to optimize the entire lumber production process and increase lumber recovery are important issues for forest products manufacturers. Therefore, a 3D log sawing optimization system was developed to perform 3D log generation, opening face determination, headrig log sawing simulation, cant resawing, and lumber grading. External log characteristics such as length, largeend and small-end diameters, diameters at each foot, and external defects were collected from five local sawmills in central Appalachia. The positions and shapes of internal log defects were predicted using a model developed by the USDA Forest Service. 3D modeling techniques were applied to reconstruct a 3D virtual log that included internal defects. Heuristic and dynamic programming algorithms were developed to determine the opening face and grade sawing optimization. The National Hardwood Lumber Association (NHLA) grading rules were computerized and incorporated into the system to perform lumber grading. Preliminary results have shown that hardwood sawmills have the potential to increase lumber value by determining the optimal opening face and optimizing the sawing patterns. Our study showed that without flitch edging and trimming, the average lumber value recovery in the sawmills could be increased by 10.01 percent using a heuristic algorithm or 14.21 percent using a dynamic programming algorithm, respectively. An optimal 3D visualization system was developed for edging and trimming of rough lumber in central Appalachian. Exhaustive search procedures and a dynamic programming algorithm were employed to achieve the optimal edging and trimming solution, respectively.;An optimal procedure was also developed to grade hardwood lumber based on the National Hardwood Lumber Association (NHLA) grading rules. The system was validated through comparisons of the total lumber value generated by the system as compared to values obtained at six local sawmills. A total of 360 boards were measured for specific characteristics including board dimensions, defects, shapes, wane and the results of edging and trimming for each board. Results indicated that lumber value and surface measure from six sawmills could be increased on average by 19.97 percent and 6.2 percent, respectively, by comparing the optimal edging and trimming system with real sawmill operations.;A combined optimal edging and trimming algorithm was embedded as a component in the 3D log sawing optimization system. Multiple sawing methods are allowed in the combined system, including live sawing, cant sawing, grade sawing, and multi-thickness sawing. The system was tested using field data collected at local sawmills in the central Appalachian region. Results showed that significant gains in lumber value recovery can be achieved by using the 3D log sawing system as compared to current sawmill practices. By combining primary log sawing and flitch edging and trimming in a system, better solutions were obtained than when using the model that only considered primary log sawing. The resulting computer optimization system can assist hardwood sawmill managers and production personnel in efficiently utilizing raw materials and increasing their overall competitiveness in the forest products market

Development of an adaptive sawmill- flow simulator template for predicting results of changes at small- log sawmills.

Managing or designing sawmills can be an extremely difficult and sawmill managers and designers face a multitude of decisions each day with regard to management of sawmill operations and productivity. Sawmill managers therefore must be skilled enough at balancing the variables that determine sawmill production including: raw materials, personnel, equipment, product mix, product quality, orders and money in order to make profits. Changing any of these variables in one part of the mill can have unforeseen and sometimes detrimental impact upon other parts of the mill. Extreme heterogeneity in raw materials adds significantly to the complexity of sawmill systems. Simulation is one of the most common methods for constructing models that include random behaviour of a large number and a wide variety of components in sawmilling such as reduced availability of large-diameter logs with increased wood demands which may result into smaller-diameter logs entering sawmills. The design and operation of a modern small-log sawmill requires skills different from those needed in a large-log sawmill. Because the log size is small and lumber production per log is low, production must be high. Profitable sawing of small diameter logs requires high speed processing, use of curve sawing, and careful loggeometry and orientation considerations before sawing. Although numerous simulation studies have investigated sawing process of largediameter logs, only a limited number of simulators have addressed processing of smalldiameter logs. Further, these latter simulators concentrated on improving either the lumber volume yield or the lumber grade/value from logs. The modeling of entire sawmill operations has been far less extensive. The sawmill-flow simulator template (SFST) and a simulation template end–user interface designed on Excel spreadsheets in this study is a unique modeling package that can be used to predict results of changes in production at a small-log sawmills. The SFST encompasses log–sawing and sawmill-flow logics designed to facilitate flexibility in modeling different sawmill configurations and production scenarios. These may include predicting the impact on sawmill performance measures due to changes in mill layout, raw material and product pecifications, sawing solutions, and queue sizes which can greatly help the saw miller to make ellinformed decisions. Keywords: Sawmill flow simulator – modular approach – discrete event simulationTanzania Journal of Forestry and Nature Conservation Vol. 77 2008: pp. 73-9

Life cycle analysis of forest carbon in the central appalachian region

Forest management and wood product processing activities such as harvesting, transportation, and lumber processing consume fossil fuels and emit carbon dioxide. This emitted carbon dioxide creates credit carbon balance which is usually overlooked while estimating the carbon benefits from woody biomass and wood products. Accountability of carbon stored in woody biomass and wood products varies when such carbon emissions are considered. Factors such as, harvesting intensity, growth rate, dead trees and forest fires all affected the estimation of forest carbon balance while harvesting system determines the carbon emission from fossil fuel consumptions. Energy sources used in sawmills for electricity are also crucial in credit carbon balance analysis. Therefore, this study assessed (1) forest carbon balance of the mixed Appalachian hardwood forests and carbon emissions due to the use of fossil fuels in harvesting systems in West Virginia, and (2) carbon balance in hardwood lumber processing in the central Appalachian region. Data were obtained from a regional sawmill survey, public database and relevant publications.;Forest carbon balance and carbon emission were analyzed within a life cycle inventory framework of cradle to gate using sensitivity analysis and stochastic simulation. The results showed that the annual carbon balance of the forests per hectare was not significantly affected by carbon loss from the volume of removal, fire and dead trees. It was also found that carbon emission from combustion of fossil fuel using manual harvesting system was less than using mechanized harvesting systems. Though a minimal amount of carbon was emitted from harvesting systems, the forest carbon displacement rate during timber processing was affected largely by hauling compared to felling, processing, skidding and loading. Carbon emission quantity from fuel consumption and forest carbon displacement rate were also affected by harvest intensity, hauling, payload size, forest type, and machine productivity.;Credit carbon balance generated from lumber processing was statistically analyzed within the gate to gate life cycle inventory framework. Stochastic simulation of carbon emission and its impact on carbon balance and carbon flux during lumber processing were carried out under different operational scenarios. Credit carbon balance from electricity consumption varied among sawmills of different production levels and operation hours per week and also attributed effect of different head saws, lighting types and air compressors used at sawmills. Credit carbon balance significantly reduced the carbon accountability of the lumber in useful life period at first order of decay of carbon. Substantial amount of carbon flux attributed from energy consumption and exports of lumber reduced the carbon storage accountability of the lumber product. Increase of the carbon accountability of the lumber products and decrease of the carbon flux ratio could be achieved through using an efficient equipments at sawmills and an appropriate mixture of energy sources for electricity supply

Development of A 3D Log Sawing Optimization System for Small Sawmills in Central Appalachia, US

A 3D log sawing optimization system was developed to perform log generation, opening face determination, sawing simulation, and lumber grading using 3D modeling techniques. Heuristic and dynamic programming algorithms were used to determine opening face and grade sawing optimization. Positions and shapes of internal log defects were predicted using a model developed by the USDA Forest Service. Lumber grading procedures were based on National Hardwood Lumber Association rules. The system was validated through comparisons with sawmill lumber values. External characteristics of logs, including length, large-end and small-end diameters, diameters at each foot, and defects were collected from five local sawmills in central Appalachia. Results indicated that hardwood sawmills have the potential to increase lumber value through optimal opening face and sawing optimizations. With these optimizations, average lumber value recovery could be increased by 10.01% using the heuristic algorithm or 14.21% using the dynamic programming algorithm. Lumber grade was improved significantly by using the optimal algorithms. For example, recovery of select or higher grade lumber increased 16-30%. This optimization system would help small sawmill operators improve their processing performance and improve industry competitiveness

Integrating Economic Performance and Process Simulation Models in Evaluating Sawmill Design Alternatives

This paper describes a simulation study that combines an economic performance measure and a process simulation model. This integrated approach is capable of capturing the operational and cost behavior of a sawmill system over time by taking into consideration the effects of the stochastic occurrence of machine breakdowns and other processing delays. The method is demonstrated using an actual design problem involving a profiler chipper-canter mill

BIOMASS PRODUCTION AS AN ENERGY SOURCE IN COPPICES OF THE PROVINCE OF FLORENCE, ITALY: CONSIDERING THE ECONOMIC AND EMPLOYMENT ASPECTS

Coppice management of forests in Tuscany, and particularly in the province of Florence, has had a chequered history, which has set the stage for the present problems of forestry policy. In Italy, the period between 1955 and 1975 saw a marked reduction in use of firewood in the home and its virtual elimination from the industrial context, leading to progressive abandonment of coppice management. Since 1975, wood-cutting has once again become an increasingly frequent practice, mainly in beech and deciduous oak forests, to the point that this phenomenon has been defined as a veritable revival of coppice management. This change is due partly to a rise in firewood prices but also to the greater yield obtainable from processing activities, although the observed increase is to be attributed not so much to technological progress (only a few enterprises have adequate equipment), as to the mass accumulated during the period of non-harvesting. Basing our opinion on these considerations we realized a project for assessing the "actual" economic, occupational and environmental potential of coppice management in the province of Florence for biomass production as an energy source. To this end, our work has tried to identify the new potential market area, the enterprises typology, and analyse the social and environmental impact. Moreover, we have evaluated economic efficiency and have taken into consideration the public intervention needed to develop new markets. We have seen that at the moment the possible market areas are tied not only to the traditional markets for house heating by traditional stove with low performance, but also to a new market tied to the most recent developments in heating technologies for dwelling places and small environments, that have allowed considerable technological improvement in heating systems using wood biomasses, which are now more economical and easier to use, have lower gas emission levels and offer greater safety. Another potential market is tied to the electric power production, through the transformation from energy produced by combustion of wood biomass into electric power energy, that we can use in times of peak power consumption in the area studied. The results of this study shows that use of wood biomasses in the energy sector is competitive with oil and gas fired systems, and that biomass production as an energy source is not only environmentally sustainable but also economically feasible and capable of creating job opportunities. Moreover, the possibility for development of two new market areas exists: the first is tied to production of heating energy with new technology plants that use a wood biomass, and result in a high performance; the second is the possibility to develop the electric energy market with the use of gasification or cogeneration plants. In both cases it is possible to create new activities for installation, maintenance and fueling of heating plants, for maintenance and fueling electric generator plants, which will be complementary to harvesting activityResource /Energy Economics and Policy,

The Lean Index: Operational "Lean" Metrics for the Wood Products Industry

No standard definition for lean production exists today, especially specific to the wood products industries. From a management point of view, even the more straightforward management issues surrounding the concept of "lean" are complex. This exploratory research seeks to develop a methodology for quantitative and objective assessment of the leanness of any wood products operation. Factor analysis is a statistical approach that describes the patterns of relationships among quantifiable predictor variables, with the goal of identifying variables that cannot be directly measured, such as the leanness of a company. Using this technique, a factor model was identified and a factor score, or "Lean Index," was developed. For the nine wood products companies included in this study, the average Lean Index is demonstrated to be 5.07, ranging from a low of 2.33 to a high of 12.00. Based on the quantified standards of lean production developed in this study, (1) primary wood products operations are inherently leaner than secondary wood products operations; (2) process throughput variables explain approximately twice the total variance of all consumed resources, compared to process support variables; and (3) energy consumption is shown to be the single most significant contributor to the leanness of any wood products company

Development of Enhanced Emission Factor Through the Identification of an Optimal Combination of Input Variables Using Artificial Neural Network

A great deal of attention is being paid worldwide to particulate matter (PM), which is now considered a significant component of air pollution. Specifically, in this thesis, road dust is a primary source of PM that is having a significant impact on human health and air quality. For example, impaired visibility due to road dust can cause more vehicle accidents. Hence, in order to efficiently develop PM control strategies, it is critical to improve the estimation of PM concentration levels generating from paved and unpaved roads. Since 1979, the U.S. Environmental Protection Agency (EPA) has developed emission factor equations to quantify the magnitude of PM for paved and unpaved roads based on multiple linear regression (MLR) models. However, the MLR models are not suitable for PM data that exhibit the characteristics of complexity and non-linearity, thereby limiting the predictive accuracy of MLR to estimate PM. The objective of this thesis is to present a method to improve the quality of the existing EPA emission factor equations for paved and unpaved roads by employing an artificial neural network (ANN). The proposed method consists of the following steps: data processing for outliers, data normalization, data classification, ANN model training to determine the weights of emission factors identified, and method validation through additional data testing. This thesis included a case study using the data retrieved from the database used by the EPA to generate their emission factor equations for paved and unpaved roads. The proposed method was evaluated by demonstrating its improved performance as shown in the coefficient of determination (R2) and the root mean square error (RMSE) values compared to the values obtained with the existing EPA emission equations. The empirical findings of the case study verified that the proposed method using the ANN model is capable of improving the quality of the EPA emission equations, resulting in higher R 2 and lower RMSE values for both paved and unpaved roads. The expected significance of this thesis is that the proposed method improves the ability to develop more reliable emission factors for predictable PM levels that can help agencies establish enhanced PM control strategies. In addition, the method may have application in other fields that require a selection process to identify an optimal combination of input variables