Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Industrial and Managements Systems Engineering

Committee Chair

Bhaskaran Gopalakrishnan

Committee Co-Chair

Robert Creese

Committee Member

Shawn Grushecky

Committee Member

Jingxin Wang

Committee Member

Feng Yang


Energy costs have risen immensely in the recent past and have strained US industrial sectors. The forest products sector is considered as an energy intensive industry group and energy use has an important impact on sawmill's financial integrity. Energy intensity or specific energy consumption (SEC) is an important aspect to wood products producing sawmills since it also represents production efficiency to some extent. This research focuses on developing SEC profiles for the manufacture of hardwood lumber in sawmills and estimating energy intensity based on product, process and system parameters. Energy benchmarking will help the sawmill industry to know their level of performance and opportunities to improve their energy efficiency and productivity. Process, production and energy data were gathered by visiting three sawmills with single sawing lines and two sawmills with double sawing lines in West Virginia.;Initially SEC was calculated in the traditional way as total energy consumption by total board feet sawn and the average SEC for all the sawmills was around 100 kWh per thousand board feet of lumber sawn. Effect of lumber sizes sawn on energy consumption was analyzed and a method to calculate SEC based on surface area sawn was developed. Sawmills' SEC developed based on surface area sawn yielded better results than traditionally calculated SEC since it exposed production bottle necks.;Data from four sawmills was used to develop three estimation models to estimate SEC of the fifth sawmill based on product, process and system parameters. The parameters that were included in the model were: species and lumber sizes for product, sawing time and maintenance schedule for process, and motor horse power, availability of resaw and production line configuration for system. The model which had 'motor horse power x minutes' as one of the estimator variables was better than the other two models in terms of both R2 and ability to estimate SEC of the fifth sawmill. One estimation model was developed to predict total energy consumption and although this model had the highest R2, it didn't estimate the fifth sawmill that well. Sensitivity analysis was conducted to find the effect of different widths of lumber sawn on energy consumption and also the parameters used in the estimation model were analyzed for their sensitivity towards the energy consumption. Energy consumption of Sawmill 3 was highly sensitive to estimator variables 'motor horse power' and 'grade lumber sizes'. Energy consumption of sawmill motors were compared and the highest energy consumer of sawmill 2 and 4 motors was main saw and carriage feed, since there was no resaw or a gang saw in them. The energy consumption of sawmill 1 motors was similar to sawmill 3 and energy consumption of sawmill 2 motors was similar to sawmill 4.;The 'Sawmill Energy Estimation Program' that takes the inputs from the user and estimates sawmill's energy intensity based on sawmill parameters and analyzes sawmill's efficiency and gives recommendations with estimated savings to improve sawmill's energy efficiency and productivity was also developed to help sawmill owners to analyze their sawmill.