Brent R. Young

Experimental determination of transverse mixing kinetics in a rolling drum by image analysis

Abstract Rotary kilns are commonly used for mixing of solids, such as grain, and heat transfer to solids, such as drying of fruit and calcination of cement. For a rotary kiln that has a wide range of solid feeds, it is desired to be able to know the kinetics of the mixing of solids inside the rotary kiln so that heat transfer between these solids can be predicted. Heat transfer between different solid feeds is particularly important in the rotating kiln being commissioned by Stuart Energy in Gladstone, Queensland for the processing of oil shale to produce oil. This paper describes a new way to determine the mixing rates of solids in a rotating drum using image analysis programming. Results of this analysis show that the mixing dynamics follow a constant rate until a completely mixed state is encountered. Upon closer analysis it was revealed that mixing occurred in steps, which has not been previously shown.

Fuzzy logic modeling of surface ozone concentrations

Abstract Due to the complex relationships and the necessity for forecasts in atmospheric studies, air pollution modeling is a task for which fuzzy logic methods are amicably suited. This research investigates the ability to predict surface ozone concentration with the use of an automated fuzzy logic method, termed modified learning from examples (MLFE). Hourly ozone concentrations during summer months in the city of Edmonton are predicted with MLFE models and the results are compared to models used by Environment Canada. The root mean square error, mean absolute error and scatter plots are used to compare the results of the MLFE, CHRONOS and CANFIS models. The newly developed model captures the trends in ozone concentrations, and based on the statistical comparisons, the MLFE consistently shows good agreement with the measured data. The MLFE model compares favourably with CHRONOS and CANFIS and is easier to implement.

PLANTWIDE CONTROL STUDY OF A VINYL ACETATE MONOMER PROCESS DESIGN

ABSTRACT A simulation of a vinyl acetate monomer (VAM) process design was developed and compared with the work of Luyben and Tyreus (1998). Two incremental changes were made to the two main control substructures. Specifically, the two schemes focus on improving the liquid inventory system control and controllability of the azeotropic distillation column. The level control strategy was tested and found to produce a faster response with less oscillatory behavior. Two alternative control techniques for the azeotropic distillation column were tested, a feed-forward model predictive controller and a static feed-forward ratio controller. The model predictive controller results illustrated the large difference between the water composition analyzer sample time and the controller step size. The static feed-forward ratio controller showed excellent disturbance rejection of large feed flow variations to the azeotropic distillation column. Simulation results are presented to illustrate the effectiveness of the new control strategies.

Real-Time Computer Simulation Workshops for the Process Control Education of Undergraduate Chemical Engineers

Realistic workshops involving real‐time simulation of chemical processes are introduced for an undergraduate process‐control course in chemical engineering. The workshops are based on fundamental‐process models of industrial unit operations and are designed for the “hands‐on” learning of process control. These workshops can be used in a computer laboratory under all readily available commercial process simulation software, namely, HYSYS, Aspen Dynamics, and MATLAB. This paper reviews these workshops and how they are used for effective process control education of chemical engineers.

Heat capacities and enthalpies for some Australian oil shales from non-isothermal modulated DSC

The application of thermal analysis to Australian oil shales has been quite common, however, the results have been somewhat limited by experimental technique and advances in thermal analysis instrumentation. In this paper we present a novel approach to the thermal characterisation of Australian oil shale. This approach involves separation of the unique components of oil shale, the kerogen (organic component) and the clay minerals (inorganic components), using chemical and physical techniques. The heat capacity and enthalpy changes for the kerogen and clay minerals were measured using non-isothermal modulated DSC from 25 to 500°C. Heat capacity data was obtained over a temperature range spanning several hundred degrees in a single experiment. Heat capacity was also estimated by incorporating TG data during regions where thermal reactions involving mass loss occurred. Enthalpy data for dehydration and pyrolysis of kerogen were also determined.

Modelling Transverse Segregation of Particulate Solids in a Rolling Drum

Segregation of solids occurs when solid particles of varying sizes and/or densities are mixed. The size and/or density differences result in heavier and/or smaller particles being concentrated toward the centre of the solid bed in a rolling drum to form a segregated core. Segregation has important energy efficiency implications in the operation of industrial rotating kilns. In this paper, a model is developed which allows the prediction of the transverse segregation of granular material in a rolling drum. The model was derived from new experiments employing image analysis. The independent variables in this work were the particle size ratio and the operational parameters of the drum and thus the model could be easily and validly applied to practical situations. The ability of the model to predict the final steady state segregation configuration was tested against independent data and good agreement was observed.

A completely real time approach to process control education for process systems engineering students and practitioners

Abstract The traditional approach to process control education of process systems engineers has been to employ the classical methods of process control that were originally developed as a substitute for the real time simulation of process systems. It is our contention that with the avialability of fast and easy-to-use simulation software, classical methods have limited relevance for the process control education of practicing process systems engineers. In this paper, we will outline our real time approach to process control instruction. The methodology is, then, illustrated by application to the feedback control of liquid level in a separator. Finally, the results of student subject evaluations from implementation in both the senior undergraduate process control course at the University of Calgary and in the AEA Technology Engineering Software process control training course are presented.

Rectification of plant measurements using a statistical framework

Abstract Data rectification is the process of removing errors from the measured process data and estimating the true state of the plant. In this study, data rectification is posed in a probabilistic framework and historical plant data is utilized to learn the parameters of the plant model. This approach finds the most likely estimates of the true process states by maximizing the probability of the process states given the measurements. Using Bayes’ theorem, this maximization is redefined as the product of the prior probability density function (pdf) of process states and the probability distribution of measurements given the true process states. The technique exploits the existing trade off between these two terms to find the most likely values for the measured process variables. The method of adaptive mixtures is used for both off-line and on-line estimating and updating of the pdf of the measured process variables. It is a recursive nonparametric method that fits a mixture of Gaussian pdf’s to the data. The changes in the process operating conditions are reflected by adding new components to the mixture. The maximum likelihood data rectification objective function consists of two pdf’s. One represents the likely process states and the other characterizes the likely adjustments to the measured values. The first pdf, given the measured data condition, is estimated by the expectation–maximization (EM) algorithm or adaptive mixtures. The second pdf is modeled by the product of bimodal Gaussian distributions each representing a process sensor. The resultant complex objective function is then maximized by an iterative EM algorithm and the most likely values of the measured process variables are found. The changes in process operation are reflected in the objective function by updating its terms. The pdf of the measured process states is updated using the rectified points and a robust approach has been developed to update the pdf’s representing the plant sensors operation. This new approach is implemented for numerical and chemical process examples. The capabilities of the new approach are demonstrated by producing robust estimates for covariance matrices, reliable estimates of mixture probability densities, rejecting the errors in the measurements and yielding reliable rectified values for the measured process variables in these examples.

Comparison of transverse mixing kinetic data obtained from a rolling drum

Abstract Cursory correlations between data obtained in different rotating drums are not valid without detailed modelling and a complete understanding of all experimental variables.

Control loop noise rejection using fuzzy logic.

This paper describes an application of fuzzy logic to noise rejection in a control loop. This new use of fuzzy logic solves the problem of sluggish control loop response when using a set-point range to stop constant valve chattering due to noise in the output signal being sent to a control valve. Multiple related variables and a general understanding of their inter-relationship must be available for this method to be successfully applied. An overview of the specific fuzzy logic method used for this application is presented along with guidelines for the practical application. In addition, this paper includes results from the successful implementation of fuzzy logic to a control loop on a pilot plant distillation column.