Tools for Teaching Batch Distillation Inductively using Process Simulation

One approach to active learning involves taking students through a guided exploration process; one which ensures students will observe phenomena and then be asked questions intended to draw conclusions regarding the observations. In recent years, this has been called inductive teaching, but more recently the expression “inquiry-based instruction” or related terms have become more common. Concurrently, simulation is becoming an increasingly important tool to perform these guided explorations as constrained resources prevent operation of laboratory equipment during lecture-oriented classes. This paper describes the development of a tutorial to teach students how to develop and conduct simulations using Aspen Batch Distillation, along with the design of four inquiry activities modeled after the work of Vigeant and Prince1-2. This model begins with consideration of a scenario, followed by prediction, exploration, conclusion, and reflection. The four inquiry activities are designed to explore key relationships in batch distillation involving pressure, heating rate, column internals, and reflux ratios, and to also consider the safety and economic factors in batch distillation design and operation. The tutorial and activities (complete with suggested solutions) will be made available to faculty members upon request while in the refinement and testing stages during fall 2013. Introduction As computers have become more capable of accurately simulating complex physical activity, traditional engineering laboratories have moved away from the laboratory and towards the virtual realm. Using simulation, an exploratory approach to learning is not hampered by physical resource limitations and time constraints. This paper describes the combination of a tutorial for batch distillation simulation with tools to engage students in an inductive learning process (the process of observation and interpretation based on factual evidence leading to generalized conclusions) and an optional experiential exercise incorporating experimental design. A part of the aspenOne family of simulation software developed by Aspen Technology, Aspen Batch Distillation3 may be used to teach the relationships of key batch distillation variables upon system performance. Using the approach described here, a student is guided through a detailed tutorial to model a laboratory batch distillation column, and then uses the results to predict the column’s performance. If the instructor would rather not have students invest time in developing the model, a complete model can be provided to students. Four inquiry-based activities have also been developed in which the effects of column pressure, heating rate, column internals, and reflux ratio are explored through simulation. The student is first asked to predict the effect of changing a process variable. Next, the student runs simulations to test their prediction. After observing these effects, the student is asked to apply what they have learned using several “whatif” type questions involving the key variables of batch distillation as well as economic and safety questions. As an optional activity, suggested protocols for twoand three-dimensional P ge 23252.2 experimental designs are made available for verification of simulation accuracy via laboratory experimentation on a modeled column. This experimentation will likely reveal (as it did in our laboratory) the difficulty of accurate simulation and stresses consideration of sources of experimental error which are not incorporated into a particular simulation. Background Teaching can be generally classified as occurring by one of two methods: deductive or inductive. For the higher education in the United States, deductive instruction has long been the dominant form. This style involves the teaching of the conclusive description of a topic and then applying that description to specific scenarios. For example, the theory behind heat transfer is learned first and the student is then expected to apply this theory to a particular real-world design. This approach is essentially the opposite of the process by which a particular body of knowledge was originally developed. Inductive learning, on the other hand, involves the acquisition of knowledge through specific observation (see Figure 1). This learning style is the more natural form of learning in that human beings are creatures of observation. By observing complex phenomenon for particular situations, broader descriptions, models, and theory may be developed. Deductive learning still has an important part in engineering education. In fact, a pure deductive or inductive learning style is neither efficient nor effective on its own. Therefore, the highest level of learning occurs with a combination of the two styles. To pique student interest and improve motivation, it is recommended that the inductive learning style precede the deductive style5. This arrangement would first require the student to observe some phenomenon and induce specifics from these observations. A deductive style would then be used to dissect the phenomenon and educate the student on the workings of the constituent parts and how they function as a whole. This combined learning style is called “student-centered” learning and depends heavily on student involvement. This combination is the justification for use of the inquiry-based methods described later. Module Objectives The modeling of real batch distillation systems is mathematically intensive and often requires numerical solutions. Engineering software packages can be used to familiarize students with practical design parameters associated with batch distillation units and to teach to role of key process variables (reboiler power input, reflux ratio, and operating pressure) on system operation. Nearly 95% of the chemical industry consists of processes with at least one distillation unit6, with most operating in continuous mode. It is, however, becoming increasingly important that chemical engineering students develop a firm grasp on batch distillation principles because of the dependence of the pharmaceutical, food, and other high-purity or high material Figure 1. Inductive and deductive learning4.