A study of energy consumption and efficiency for Appalachian hardwood sawmills was conducted in the Appalachian region. Primary data were collected through a mail survey on sawmills in the region in 2010, while secondary data were obtained from site audits at 17 sawmills over the last 10 years in West Virginia. The results from the mail survey showed that hardwood lumber production volume ranged from 700 to 600,000 board feet (BF) per wk, and monthly electricity consumption per mill averaged 220 kWh per thousand board feet (MBF) with an average electric bill of $17.78/MBF/mo. The energy audit results indicated that hardwood lumber production volume ranged from 4,250 to 400,000 MBF/y, and the energy use and total cost per thousand board feet of lumber production averaged 160.89 kWh/MBF and $10.04/MBF, respectively. The average marginal cost for all energy audits was $17.87/MMBtu (¢6.10/kWh). The annual carbon dioxide emission conserved was 587,045 pounds per mill. On average, engineers on site visits proposed changes that could save approximately 14.89 percent of the annual energy used. The results presented in this article provide energy profiles for Appalachian hardwood sawmills and reveal some potential techniques for reducing energy consumption.

Hot dip galvanizing facilities have energy intensive operations with electrical and gaseous or oil fuel energy representing a significant share of total energy usage. Benchmarking galvanizing energy consumption and understanding the specific energy consumption is important. The enhanced galvanizing energy profiler decision support system (E-GEPDSS) was developed to identify specific energy consumption through comprehensive heat balance analysis. The results presented in this article will assist users make energy efficient decisions for product, process and system level parameters. This research on the analysis of galvanizing operations focused on the annealing furnaces and zinc coating pot (hereafter referred to simply as the “pot”) for energy consumption. The heat balance model was developed and applied using data collected from various manufacturing plants. Sensitivity analysis was performed to study the impact on heat loss from the hot dip galvanizing system of changing the product, process and system level parameters. Based on the results produced by E-GEPDSS for the manufacturing plants, it was found that less than 50% of heat supplied to the furnace is absorbed by the product; the remainder of the input heat energy dissipates as losses. Heat loss from the wall surfaces and flue gas stack was found to be significant, while heat lost due to opening and phase changes were minimal. In the case of zinc pot analysis, when there is no pre-melt pot, the largest heat loss was associated with the ingot melting process and the energy system required to maintain constant pot temperature. When there is a pre-melt pot in the process, the largest heat loss in the main pot is from zinc coating. Using the heat balance model, galvanizing manufacturers will be able to identify opportunities to reduce these losses for energy savings and optimize energy system performance. Emissivity, dimensions of the furnace, pot temperatures, furnace zone temperatures, thermal conductivity of insulation materials and steel strip temperature at the entry and exit at each zone have significant impact on the total heat loss.

Energy plays a central role in the operation of the industrialized U.S. economy. The importance of the energy assessment process and the opportunity generated for industry to reduce energy consumption and increase energy efficiency cannot be overemphasized. The energy assessment process in a large-sized manufacturing facility containing a plethora of interacting energy consuming systems and equipment may be complicated if pursued in a non-structured manner. The plant-wide energy assessment process is an important step in this regard. The research reported in this article, based on an actual assessment of a large-sized manufacturing facility, outlines not only the need for a systems approach towards plant-wide energy assessment, but also describes the elements of this proposed systematic approach.

This paper addresses the question “Is energy that different from labor?” from the perspective of efficiency. It presents a novel statistical analysis for the auto assembly industry in North America to examine the determinants of relative energy intensity, and contrasts this with a similar analysis of the determinants of another important factor of production, labor intensity. The data used combine two non-public sources of data previously used to separately study key performance indicators (KPIs) for energy and labor intensity. The study found these two KPIs are statistically correlated (the correlation coefficient is 0.67) and the relationship is one-to-one. The paper identifies 11 factors that may influence both energy and labor intensity KPIs. The study then contrasts which of the empirical factors the two KPIs’ share and how they differ. Two novel statistical methods, Huber estimators and Multiple M-estimators, combined with regularized algorithms, are identified as the preferred methods for robust statistical models to estimate energy intensity. Based on our analysis, the underlying determinants of energy efficiency and labor productivity are quite similar. This implies that strategies to improve energy may have spillover benefits to labor, and vice versa. The study shows vehicle variety, car model types, and launch of a new vehicle penalize both energy and labor intensity, while flexible manufacturing, production volume, and year of production improve both energy and labor intensity. In addition, the study found that the plants that produce small cars are more energy-efficient and productive compared to plants that produce large vehicles. Moreover, in a given functional unit, i.e., on a per-unit basis, Japanese plants are more energy-efficient and productive compared to American plants. Plant managers can use the proposed data-driven approach to make the right decisions about the energy efficiency targets and improve plants’ energy efficiency up to 38% using hybrid regression methods, mathematical modeling, plants’ resources, and constraints.