Water Content Determinations for Peat and Other Organic Soils Using the Oven-Drying Method

There has been much debate in the literature over the past 60 years regarding an appropriate oven-drying temperature for water content determinations in peat and other organic soils. For inorganic soils, the water content is usually based on the equilibrium dry mass corresponding to drying temperatures in the range 100-110°C. However, for peat and other organic soils, several researchers have recommended lower drying temperatures in the range 60-90°C in an attempt to prevent possible charring, oxidation, and/or vaporization of substances other than pore water. However, all of the relevant water is not fully evaporated at too low a temperature, and because specimen dry mass is a function of drying temperature, the resulting water content values are lower than those determined for the temperature range 100-110°C. Experimental data reported in this article show that oven drying of peat and other organic soils at 100-110°C using either gravity-convection or forced-draft ovens is acceptable for routine water content determinations. Because a standardized oven temperature is desirable when correlating water content with other material properties, it is recommended that oven drying of peat and other organic soils be performed over temperature ranges of either 105-110°C or 105 ± 5°C, in line with standardized ranges for inorganic soils. © 2014 Copyright Taylor & Francis Group, LLC.

Advanced Modelling and Optimization of Infared Oven in Injection Stretch Blow-moulding for Energy Saving

In the production process of polyethylene terephthalate (PET) bottles, the initial temperature of preforms plays a central role on the final thickness, intensity and other structural properties of the bottles. Also, the difference between inside and outside temperature profiles could make a significant impact on the final product quality. The preforms are preheated by infrared heating oven system which is often an open loop system and relies heavily on trial and error approach to adjust the lamp power settings. In this paper, a radial basis function (RBF) neural network model, optimized by a two-stage selection (TSS) algorithm combined with partial swarm optimization (PSO), is developed to model the nonlinear relations between the lamp power settings and the output temperature profile of PET bottles. Then an improved PSO method for lamp setting adjustment using the above model is presented. Simulation results based on experimental data confirm the effectiveness of the modelling and optimization method.