Modified oxygen consumption calorimetry was used to track the seasonal flammability of black spruce and tamarack. Age class related samples were collected for both species from May to September at research site in central Alberta. These samples were assessed for their differential heat release using test equipment at the Protective Clothing and Equipment Research Facility (PCERF) at the University of Alberta.
The test method was able to successfully quantify the differences in seasonal flammability between black spruce and tamarack. Data showed the age-related flammability differences were less pronounced, with the exception of new growth samples early in the season.
Hummingbird Network, a British Columbia company, presented its crowdsourcing wildfire detection concept (the Hummingbird Network Smoke Detection Service) during the 2016 Wildland Fire Canada conference. In January 2017, as a follow-up to the conference, Hummingbird Network provided a live demonstration to AAF, BC Wildfire Service, and FPInnovations in Edmonton, Alberta. After a successful demonstration, and at the request of the wildfire agencies, FPInnovations committed to working with Hummingbird Network to provide an evaluation of its wildfire detection system.
Mulching is a common method of fuel treatment. However, it is not currently listed by the U.S. Forest Service as a fuel type in its recommendations for fire retardant coverage levels. FPInnovations researchers set up plots with different coverage levels of retardant on a mulch fuel bed and collected fire behaviour data when a fire interacted with these plots. The results are intended to help wildfire agencies understand the effectiveness of retardant on mulch fuels in developing better suppression plans.
Data was collected within a burned out area on a steep mountain slope as part of FPInnovations’s Survival Zone project. The fire was a prescribed burn carried out by Parks Canada in Jasper National Park. The data collected shows that in this one instance, that temperatures and heat flux values fell within survivable range for firefighters wearing PPE. This report does not condone firefighters above a fire on a steep slope, but rather this PB was used as a data collecting opportunity.
This study investigated the effects of applying three mulch treatment intensities on fuel bed characteristics and the resultant fire behaviour. This is a companion report to a previously published report titled Mulching productivity in black spruce fuels: Productivity as a function of treatment intensity. The findings of these fire behaviour trials, in conjunction with productivity results, can assist fuel management practitioners in developing appropriate cost-effective mulching prescriptions.
Linear programming is a technique used to determine the best (or optimal) solution to a problem where there are a number of competing and usually interrelated choices. The technique requires that each restriction on the problem being modeled be formulated as a linear equation. The model consists of a set of linear equations with more unknowns than equations and thus there are many possible solutions. In order to determine the best of these solutions, it is necessary to decide which criteria will be used to determine the best. Once the criteria (usually maximum profit or minimum cost) is chosen, an equation is set up giving the amount each variable (or activity) contributes to the criteria. The linear program then determines which solution will maximize or minimize this criteria. The LP described in this write up was written to determine the best process and set of process conditions for converting steam exploded Aspen wood into a variety of chemical feedstocks. The LP is designed to maximize profit based on the sales value of the chemicals produced, the cost of raw materials and the processing costs incurred. The model is restricted by the raw material availability, the utility and chemical requirements of each process step, the capacity of each process step and the market requirements for each chemical produced. This report will give a detailed description of the model structure, will discuss the validity of the data used in the model as well as future requirements, will discuss the running of the model on the computer and will discuss analysis of the LP solution.