Implementation of Modeling Tools for Teaching Biorefinery (Focused on Bioethanol Production) in Biochemical Engineering Courses: Dynamic Modeling of Batch, Semi-Batch, and Continuous Well-Stirred Bioreactors
Abstract
Due to the ever-growing pressure on our planet’s natural resources to supply energy, the production of bioethanol by fermentation of lignocellulosic biomass is increasingly important in courses related to engineering and energy. Moreover, recent changes in the teaching–learning paradigm make necessary the introduction of novel teaching tools where students are the protagonist of their education. In this context, the purpose of this study is to compare the results obtained after traditional lessons with those obtained after the implementation of various computer activities based on modeling and simulation of bioreactors to teach biorefinery concepts focused on bioethanol production. Berkeley Madonna was chosen as the digital simulation software package because it is user-friendly, fast, and easy to program. This software allowed students to gain experience writing models that let optimize fermentations in well-stirred bioreactors and others bioprocess of industrial interest. The students (those who participated in the modeling-simulation classes and those who participated in traditional ones) completed a questionnaire and a cognitive test at the end of the course. Students that participated in modeling-simulation classes got a better score than students that participated in traditional classes. Therefore, the study showed the improvement in the understanding of the biorefinery concepts and the students improved their grades. Finally, students’ perception about the proposed modeling-simulation learning was also analyzed and they rated the e ciency of this new learning methodology as satisfactory. There are very few studies providing information about educational experiences regarding the development of skills for the formulation, interpretation, simplification, and use of mathematical models based on mass balances and simple microbial kinetics in biochemical engineering courses. The experience described in this work can be used by professors to plan and conduct courses based on the modeling of biochemical engineering problems.
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energies Article Implementation of Modeling Tools for Teaching Biorefinery (Focused on Bioethanol Production) in Biochemical Engineering Courses: Dynamic Modeling of Batch, Semi-Batch, and Continuous Well-Stirred Bioreactors M. Ángeles Martín-Lara 1,* and Alicia Ronda 2 1Chemical Engineering Department, Faculty of Sciences, University of Granada, 18071 Granada, Spain 2Chemical and Environmental Department, Higher Technical School of Engineering, University of Seville, 41092 Seville, Spain; [email protected] *Correspondence: [email protected]; Tel.: +34-958-240-445 Received: 30 September 2020; Accepted: 2 November 2020; Published: 4 November 2020 Abstract: Due to the ever-growing pressure on our planet’s natural resources to supply energy, the production of bioethanol by fermentation of lignocellulosic biomass is increasingly important in courses related to engineering and energy. Moreover, recent changes in the teaching–learning paradigm make necessary the introduction of novel teaching tools where students are the protagonist of their education. In this context, the purpose of this study is to compare the results obtained after traditional lessons with those obtained after the implementation of various computer activities based on modeling and simulation of bioreactors to teach biorefinery concepts focused on bioethanol production. Berkeley Madonna was chosen as the digital simulation software package because it is user-friendly, fast, and easy to program. This software allowed students to gain experience writing models that let optimize fermentations in well-stirred bioreactors and others bioprocess of industrial interest. The students (those who participated in the modeling-simulation classes and those who participated in traditional ones) completed a questionnaire and a cognitive test at the end of the course. Students that participated in modeling-simulation classes got a better score than students that participated in traditional classes. Therefore, the study showed the improvement in the understanding of the biorefinery concepts and the students improved their grades. Finally, students’ perception about the proposed modeling-simulation learning was also analyzed and they rated the efficiency of this new learning methodology as satisfactory. There are very few studies providing information about educational experiences regarding the development of skills for the formulation, interpretation, simplification, and use of mathematical models based on mass balances and simple microbial kinetics in biochemical engineering courses. The experience described in this work can be used by professors to plan and conduct courses based on the modeling of biochemical engineering problems. Keywords: biofuels; bioprocesses; challenge-based learning; computer-based learning; energy engineering; higher education 1. Introduction With an ever-growing population, the pressure on our planet’s natural resources to supply materials, chemicals, and energy continues to rise. A biorefinery produces biofuels and/or biochemicals and/or bioenergy from biomass using different conversion methods, mainly thermochemical and/or biochemical conversion procedures [ 1 ]. Inside biofuels, the bioethanol represents good energy Energies 2020,13, 5772; doi:10.3390/en13215772 www.mdpi.com/journal/energies
Energies 2020,13, 5772 2 of 16 properties, contributing to fossil primary energy savings (in automotive industry applications, it can replace gasoline for spark-ignition engines) [ 2 , 3 ]. Some studies indicate that Europe bioethanol production for fuel purposes has increased from 9.2% in 1998 to over 70% in 2009 [4]. Therefore, in the last years, the production of bioethanol by fermentation of lignocellulosic biomass is widely explained in courses related to engineering and energy. Moreover, due to the incorporation of the University to European Higher Education Area (EHEA), numerous changes in the teaching–learning models have been made [ 5 , 6 ] and the students must be the protagonist of their education, being the professor, the supervisor aiding their acquisition of skills [ 7 , 8 ]. The students take responsibility for their own learning, being active discoverers and constructors of their own knowledge, supporting any learning method that works [ 6 , 9 ]. Authors such as Venkatraman et al. [ 10 ] indicated that the world is experiencing the fourth industrial revolution and that education systems need to be redesigned in order to meet the new needs, due to the level of automation in the industry that has increased in the last decades. Science, Technology, Engineering, and Mathematics (STEM) students have to deal with complex models of real-life systems [ 11 , 12 ]. Thus, the incorporation of novelty tools during higher education lessons improves the degree, plans and encourages the work-based learning, besides bringing students closer to their future job. These kinds of activities promote the autonomous work of students and they can reinforce their knowledge, modifying or increasing the level of the proposed activities. In the same way, the professor can contrast the evolution of students analyzing the obtained results [ 8 ]. In this work, the aim of the authors is to analyze the results obtained after the implementation of modeling tools for teaching biorefinery in biochemical engineering courses. In this context, when planning biochemical engineering courses, new methodologies have to be included in the course programs to close the gap between developers’ vision and the real-life application in STEM students [ 13 ]. Educational institutions usually rely on laboratory practices to build successful students. However, computer skills involving various software are not taught in sufficient detail, and in most cases, students will face more work with practical software tools than with experimentation in the laboratory, as Venkatraman et al. cited in their study [ 10 ]. Moreover, one of the most common complaints from students is the low amount of practical activities with these kind of tools. Hence, the STEM degrees must strive to provide more practical learning [ 11 , 14 ]. In this sense, the proposed methodology aims to fill this gap in computer skills in biochemical engineering courses. Mathematical modeling has always been a fundamental methodological procedure to solve problems in engineering, allowing the representation of the problem in an appropriate way for its treatment with specific software. In biochemical engineering, the modeling is key to understand the real fermentation process and to know the mechanisms involved during the production of bioethanol as an energy source. Moreover, the modeling is necessary in the design of industrial-sized equipment; in the control and optimization of the processes; and in the running of simulations as an alternative to expensive experiments in development laboratories [ 15 , 16 ]. Therefore, the insertion of modeling tools in STEM courses can be a useful teaching tool for students. The objective of this innovation was to give the students insight and skills in the modeling tools based on mass balances and simple microbial kinetics for the study of batch, semi-batch, and continuous well-stirred bioreactors for the production of bioethanol in fermentation-based biorefineries. Although, usually, process engineers and scientists use simulation models to investigate biorefinery operations, a recent literature review recognized only a few studies that compared simulations in biorefinery teaching with the traditional classroom teaching [ 5 , 17 – 21 ]. For example, Ruiz-Ramos et al. [ 17 ] reported the use of ASPEN Plus ® to design and assess a biorefinery from olive-derived biomass. The survey also suggested that students found this learning approach very useful. Bonde et al. [ 18 ] developed and tested a gamified biotech laboratory simulation platform. Potratz [ 19 ] demonstrated the value of simulations to enhance students’ knowledge of enzyme kinetics dynamic in an upper-division biochemistry course. Lay and Swan [ 20 ] evaluated FermOpt as a tool for teaching fermentation and optimization principles. Authors found FermOpt a relaxed technique to
Energies 2020,13, 5772 3 of 16 engage student’s attention and interest. In addition, although it was not exactly a modeling-simulation practice, Serrano-Aroca [21] encouraged students with exercises in a virtual bioreactor laboratory. There are several challenges in the application of process simulation software to biorefinery teaching[ 22 ]. Therearetwobasicapproachestoperformsimulations: (1)writingadequatemathematical models with software-oriented programming or (2) using block-oriented programming capable of mathematically modeling the performance of individual unit operations [ 23 ]. The first approach deals with the building of code-based algorithms that use specific syntax rules depending on the programming language used. Berkeley Madonna falls in this group of approach as a general-purpose differential equations solver that provides an ideal environment in which adequate mathematical models to perform simulations of bioethanol production by fermentation can be written. In addition, Berkeley Madonna software offers a free version that is only slightly restricted in functionality: the code and the setup (screen layout) cannot be saved [24]. Given the context of this study, the main goal of this work is to use the Berkeley Madonna software as an innovative teaching methodology to facilitate the learning of biorefineries processes (focused on bioethanol production) to undergraduate students of biochemical engineering courses. Several examples of obtaining the bioethanol by fermentation of lignocellulosic biomass in well-stirred bioreactors to model in Berkeley Madonna are presented to enhance the motivation, the participation, and grades of the students. 2. Methodology 2.1. Framework for Applying the Innovative Methodology This research was conducted as part of a study of the incorporation of novelty tools during the higher education lessons. The innovation was conducted during 2 academic years in a course named “Biochemical Engineering Principles” which is lectured in the second course of the Biotechnology degree at the University of Granada (Spain). The modules in which the innovation was applied provide the fundamental basis of bioethanol bioengineering based on the biorefinery concept. Figure 1shows the course framework. The methodology promoted the use of the software Berkeley Madonna to solve and simulate the bioethanol production by the mathematical modeling of the fermentation process and the mechanisms involved in it. The total number of students who completed the course was 101 (55 during 2016–2017 course and 46 during 2017–2018 course). The modeling-simulation experience was carried out with 46 and 38 students in each academic year, respectively, while the rest of students received the classes following a traditional teaching method. It is important to highlight that both groups had similar content: the dynamic modeling of batch, semi-batch, and continuous well-stirred bioreactors focused on fermentation-based biorefineries and the same background, time restrictions, and teacher. The aim of this innovation was to give the students insight and skills in the formulation, interpretation, simplification, and use of mathematical models inside the framework of the biochemical engineering course. They allow students a better understanding of batch, semi-batch, and continuous well-stirred bioreactors for the production of bioethanol in fermentation-based biorefineries, based on mass balances and simple microbial kinetics for the study.
Energies 2020,13, 5772 4 of 16 Energies2020,13,xFORPEERREVIEW3of16 simulationpractice,Serrano‐Aroca[21]encouragedstudentswithexercisesinavirtualbioreactor laboratory. Thereareseveralchallengesintheapplicationofprocesssimulationsoftwaretobiorefinery teaching[22].Therearetwobasicapproachestoperformsimulations:(1)writingadequate mathematicalmodelswithsoftware‐orientedprogrammingor(2)usingblock‐orientedprogramming capableofmathematicallymodelingtheperformanceofindividualunitoperations[23].Thefirst approachdealswiththebuildingofcode‐basedalgorithmsthatusespecificsyntaxrulesdepending ontheprogramminglanguageused.BerkeleyMadonnafallsinthisgroupofapproachasageneralpurposedifferentialequationssolverthatprovidesanidealenvironmentinwhichadequate mathematicalmodelstoperformsimulationsofbioethanolproductionbyfermentationcanbe written.Inaddition,BerkeleyMadonnasoftwareoffersafreeversionthatisonlyslightlyrestricted infunctionality:thecodeandthesetup(screenlayout)cannotbesaved[24]. Giventhecontextofthisstudy,themaingoalofthisworkistousetheBerkeleyMadonna softwareasaninnovativeteachingmethodologytofacilitatethelearningofbiorefineriesprocesses (focusedonbioethanolproduction)toundergraduatestudentsofbiochemicalengineeringcourses. SeveralexamplesofobtainingthebioethanolbyfermentationoflignocellulosicbiomassinwellstirredbioreactorstomodelinBerkeleyMadonnaarepresentedtoenhancethemotivation,the participation,andgradesofthestudents. 2.Methodology 2.1.FrameworkforApplyingtheInnovativeMethodology Thisresearchwasconductedaspartofastudyoftheincorporationofnoveltytoolsduringthe highereducationlessons.Theinnovationwasconductedduring2academicyearsinacoursenamed “BiochemicalEngineeringPrinciples”whichislecturedinthesecondcourseoftheBiotechnology degreeattheUniversityofGranada(Spain).Themodulesinwhichtheinnovationwasapplied providethefundamentalbasisofbioethanolbioengineeringbasedonthebiorefineryconcept.Figure 1showsthecourseframework. Figure1.Courseframework(themeslinkedtoinvestigationaremarkedinred). BIOCHEMICAL ENGINEERING PRINCIPLES Unit Operations Mass Balances Energy Balances Heat Transfer Bioreactor Engineering Biorefinery Types ofbioreactors Effect ofprocess parameters on biological performances Industrialapplications of bioreactors Trends inbioreactor engineering Thermochemical conversion Mechanical conversion Mass Transfer Fluidflow Chemical conversion Biological conversion Fermentation Bioethanol Figure 1. Course framework (themes linked to investigation are marked in red). 2.2. Organization of the Modeling-Simulation Classes The modeling-simulation classes (a total of four sessions) were divided in five different parts each one. In the first session, a total of 4 h, was used for parts I, II, III, and IV and the second, third, and four sessions, a total of 12 h, were used to complete the most interesting part of this work, the part V. • Part I: to provide the basic theory required to understand the different modes of operation of the well-stirred tank-type bioreactors. In this part, derivations of general mass balance equations of all types of tank-type bioreactors were presented. • Part II: to provide the basic theory required to understand simple microbial kinetics. In this part, explanations of the main unstructured models for describing microbial kinetics were given. In addition, information about stoichiometry and the yield coefficient concept were provided. • Part III: to provide concrete examples to students about a model equations formulation and to explain the methodology to establish the model equations. • Part IV: to introduce the Berkeley Madonna to the students and to show how the software operates. • Part V: to solve a series of exercises using Berkeley Madonna for each one of the proposed well-stirred bioreactor. Students could work together and discuss ideas for optimization, while the teacher was available to answer questions. In the traditional learning scenario, the total time, 16 h, was used to develop Part I, Part II, and Part III. In this case, 4 h were dedicated to each part using lectures and students were taught in a manner that is conducive to sitting and listening. Only in the development of part III, the students performed some work invidually to apply the content of the subject. Table 1reports a comparison between both teaching scenarios.
Energies 2020,13, 5772 5 of 16 Table 1. Comparison between the strategies of the modeling-simulation and traditional learning scenario. Elements Modeling-Simulation Classes Traditional Classes Part I 2 h. Lectures by the teacher 4 h. Lectures by the teacher Part II 1 h. Lectures by the teacher 4 h. Lectures by the teacher Part III 1 h. Lectures by the teacher 4 h. Lectures by the teacher and individual work completed by students applying the content of the subject. Part IV 1 h. Lectures by the teacher - Part V 11 h. Problem-solving teamwork - Figure 2shows a graphical representation of the topics covered in this work including a closed-loop process of educational innovation. The aim is to evaluate the effect of the proposed methodology tools in STEM students through cognitive engagement, and due to previous studies, shows that students who are actively engaged with learning materials demonstrate greater learning gains that those who are passively engaged [9]. Energies2020,13,xFORPEERREVIEW5of16 studentswhoareactivelyengagedwithlearningmaterialsdemonstrategreaterlearninggainsthat thosewhoarepassivelyengaged[9]. Figure2.Graphicalrepresentationofthetopicscoveredinthiswork. 2.3.Modeling‐SimulationExamples/Scenarios Differentactivitieswereproposedineachexampleorscenario.Thecompleteexamplesarebased onthebookofDunnetal.[25]andMartín‐LaraandRonda[26]aboutbiologicalreactionengineering. Inordertoguidethestudentstosolvedifferentscenarios,guidelineswiththemethodologyforthe formulationofmodelequationswereproposed.Thesuggestedguidelinesarethefollowing: 1. Choosethebalanceregionsuchthatthevariablesareconstantandchangeaslittleaspossible withinthesystem.Drawboundariesaroundthechosenbalanceregion. 2. Identifythetransportstreamswhichflowacrossthesystemboundaries. 3. Writethegeneralmassbalanceinwordform. 4. Expresseachbalanceterminmathematicalformwithmeasurablevariables. 5. Introduceotherrelationshipsandbalancessuchthatthenumberofequationsequalsthenumber ofdependentvariables. 6. Foradditionalinsightwithcomplexproblems,drawaninformationflowdiagram. 7. SimulatethepracticalcaseonBerkeleyMadonnaanddiscussthesimulationvisualization. Fromthematerialsandskillsgiventothestudents,theyshouldformulatethemodelequations foreachsystemandsolvetheproposedactivities.Thefirstactivityofeachpracticeistoformulate themodelequationsthatcharacterizethesystemaccordingtospecificationsprovidedbytheteacher. ThemodelequationsforeachproposedsystemhavetobeformulatedaccordingtoBerkeley Madonnaspecifications.Inordertoincreasethecompetencesofstudentsintermsofknowledgeof thefermentationprocess,threedifferentscenarioswereproposedanddifferentunderstanding activitiesweresuggestedforeachsystem. * modesofoperationofthe well‐stirredtank‐type bioreactors *basictheoryrequiredto understandsimplemicrobial kinetics *modelequationsformulation MODELINGSIMULATION CLASSES TRADITIONAL TEACHING CLASSES SATISFACTION RESULTS COGNITIVE QUESTIONS Student satisfaction improvement? Learning outcomes improvement? Deeper understanding ofconcepts? Figure 2. Graphical representation of the topics covered in this work. 2.3. Modeling-Simulation Examples/Scenarios Different activities were proposed in each example or scenario. The complete examples are based on the book of Dunn et al. [ 25 ] and Mart í n-Lara and Ronda [ 26 ] about biological reaction engineering. In order to guide the students to solve different scenarios, guidelines with the methodology for the formulation of model equations were proposed. The suggested guidelines are the following: 1. Choose the balance region such that the variables are constant and change as little as possible within the system. Draw boundaries around the chosen balance region. 2. Identify the transport streams which flow across the system boundaries. 3. Write the general mass balance in word form. 4. Express each balance term in mathematical form with measurable variables. 5. Introduce other relationships and balances such that the number of equations equals the number of dependent variables. 6. For additional insight with complex problems, draw an information flow diagram.
Energies 2020,13, 5772 6 of 16 7. Simulate the practical case on Berkeley Madonna and discuss the simulation visualization. From the materials and skills given to the students, they should formulate the model equations for each system and solve the proposed activities. The first activity of each practice is to formulate the model equations that characterize the system according to specifications provided by the teacher. The model equations for each proposed system have to be formulated according to Berkeley Madonna specifications. In order to increase the competences of students in terms of knowledge of the fermentation process, three different scenarios were proposed and different understanding activities were suggested for each system. 2.3.1. Batch Fermentation The first strategy applied was batch fermentation. In these bioreactors, the production is not continuous and reaction conditions change with the time. In addition, some time for cleaning and filling is necessary. First, the bioreactor is organized for a charge of medium, and second, it is inoculated with cells and the cells are allowed to grow for a necessary time, such that the cells achieve the required cell density or optimum product concentrations [25]. In this scenario, students must complete several exercises. The first one was to formulate the model equations that characterize the system according to specifications provided by the teacher. In the second, students must run the model, giving concrete values to the parameters and analyzing the influence of the variation of the saturation constant, the maximum growth rate, and the yield biomass-substrate in the concentration of biomass, substrate, and product. Then, they must analyze the production of bioethanol by the microorganism because of fermentation under different conditions. Finally, from some provided experimental data, students must determine several parameters of the proposed system. 2.3.2. Fed-Batch Fermentation In semi-continuous or fed batch operation, additional supply of nutrients is fed into the bioreactor. However, no material is removed from it. This example is comprised of five different exercises, like those proposed in batch fermentation, involving changes in the operational model. The first exercise was to formulate the model equations that characterize the system according to specifications provided by the teacher. The second exercise was to show the biomass, substrate, and product concentration by running the model with proposed parameters and to study the effect of volumetric flow onto these concentrations. In the third exercise, a new model had to been proposed considering that there is inhibition by substrate. The fourth exercise was to solve the model with proposed parameters and to study the effect of the inhibition constant onto the obtained amount of product. The fifth exercise was to compare the behavior of batch fermentation versus semi-batch fermentation. Finally, the sixth exercise consisted of the application of the model to a biological nitrogen removal problem. This operation mode improves the yield versus the batch reactor, improving the control of nutrients concentration in the process. Although, it is not usually employed in the bioethanol production, it is important for students to know the differences between each possible operational scenario. 2.3.3. Continuous Fermentation In the continuous operation mode, fresh medium is added continuously to the bioreactor, while at the same time, the exhausted medium (containing the products excreted by the cells) is removed. If the bioreactor is perfectly mixed, the concentrations of nutrients, cells, and other components throughout the entire bioreactor are uniform, and are identical to the concentration of the effluent stream [ 25 ]. It is the most widely used mode in the industrial processes, including bioethanol production. Students must complete several different exercises (similar to other scenarios) and compare obtained results. The first exercise was to formulate the model equations that characterize the system, as in previous practices. The second one was to solve the model according to the proposed parameters
Energies 2020,13, 5772 7 of 16 by the teacher; moreover, students must determine some parameters and answer some understanding question about the model. The third exercise was to determine various parameters according to the proposed specifications by using the software tools. The fourth exercise was to model the behavior of both microorganism populations which interact and study the effect of the flow and the substrate concentration fed in the system. Finally, the fifth was to apply the model in a specific case and to study the behavior modifying various parameters. As a case example, Figure 3shows the outcomes of Berkeley Madonna for some different activities and scenarios (a–d). Students indicated that the outcomes of dynamic modeling simulations provided a clear and easily understanding of the process, allowing better learning of the bioethanol production. Energies2020,13,xFORPEERREVIEW7of16 providedaclearandeasilyunderstandingoftheprocess,allowingbetterlearningofthebioethanol production. (a)(b) (c)(d) Figure3.BerkeleyMadonnasimulationresultsof(a)activityfourforthebatchfermentation—specific velocityofethanolproductionusingZ.mobilis,(b)activityfiveforthebatchfermentation— determinationofthemaximumspecificgrowthrate(μ max ),thesubstrateaffinityconstant(K s ),the yieldcoefficientbiomass‐substrate(Y XS ),consideringaMonodgrowthtoobtainagoodfitofsome experimentaldata,(c)activitytwoforthecontinuousfermentation—productivityinfunctionoffeed volumetricflow,and(d)activitythreeforthecontinuousfermentation—influenceofthefeed volumetricflowandthesubstrateconcentrationinthesystem(predatory–prey)behavior. 2.4.EvaluationofModeling‐SimulationClasses Theaimofthisstudywastogivetheneededguidelinestostudentstosolvedifferentsystemsof fermentationsforthebioethanolproductionandothersbioprocessesinwell‐stirredbioreactorsas innovativeeducationalactivity,wherethestudentsmusttakeresponsibilityfortheirownlearning. Inthisway,applyingthiskindofmethodology,itisintendedtoencouragestudentstobethe protagonistoftheirteachingandtheprofessorhasaroleofasupervisorduringtheacquisitionof theirskills[8,9]. Therefore,toevaluatetheproposedmodelinthesubject“BiochemicalEngineeringPrinciples” andthestudents’perspectives,theycompletedaquestionnairewith9questionslistedinTable2, afterperformingmodeling‐simulationclasses.Theproposedquestionnairewasanswered anonymouslyintheclassroombystudents.Thequestionnairesweredesignedassumingtheywould becompletedbyallofthestudentsparticipatinginthecourse.Whendesigningthequestionnaires,a numberscalewasusedfortheanswers,wheretheyscoredatotalof9itemsonascaleof1(strongly disagree–absolutelyno)to5(stronglyagree–absolutelyyes).Thequestionnairewasadaptedfrom questionnairesprovidedintheirworksbyAl‐Moamerietal.[27],Larrañeta[28],andZhuetal.[29]. Resultsofthisquestionnairelettoidentifysomekeyareasofthemodeling‐simulationclassesthat couldbeimproved. Acognitivetestwasalsodistributedtothestudentsincludingfourcognitivequestions associatedwiththeoreticalexplanationsoffermentationsinwell‐stirredbioreactors,inordertogauge thestudents’improvementintheirlevelofknowledgeofthefermentationprocessandinvolved mechanisms(seeTable3).Finally,acomparisonbetweenthescoresofstudentsthatperformthe computeractivitiesandthoseofstudentsthatdidnotperformtheseactivitieswasmade. Figure 3. Berkeley Madonna simulation results of ( a ) activity four for the batch fermentation—specific velocityofethanolproductionusingZ. mobilis, ( b )activityfiveforthebatch fermentation—determination of the maximum specific growth rate ( µmax ), the substrate affinity constant (K s ), the yield coefficient biomass-substrate (Y XS ), considering a Monod growth to obtain a good fit of some experimental data, ( c ) activity two for the continuous fermentation—productivity in function of feed volumetric flow, and ( d ) activity three for the continuous fermentation—influence of the feed volumetric flow and the substrate concentration in the system (predatory–prey) behavior. 2.4. Evaluation of Modeling-Simulation Classes The aim of this study was to give the needed guidelines to students to solve different systems of fermentations for the bioethanol production and others bioprocesses in well-stirred bioreactors as innovative educational activity, where the students must take responsibility for their own learning. In this way, applying this kind of methodology, it is intended to encourage students to be the protagonist of their teaching and the professor has a role of a supervisor during the acquisition of their skills [ 8 , 9 ]. Therefore, to evaluate the proposed model in the subject “Biochemical Engineering Principles” and the students’ perspectives, they completed a questionnaire with 9 questions listed in Table 2, afterperforming modeling-simulationclasses. The proposed questionnairewas answeredanonymously in the classroom by students. The questionnaires were designed assuming they would be completed by all of the students participating in the course. When designing the questionnaires, a number scale was used for the answers, where they scored a total of 9 items on a scale of 1 (strongly disagree–absolutely no) to 5 (strongly agree–absolutely yes). The questionnaire was adapted from questionnaires provided
Energies 2020,13, 5772 8 of 16 in their works by Al-Moameri et al. [ 27 ], Larrañeta [ 28 ], and Zhu et al. [ 29 ]. Results of this questionnaire let to identify some key areas of the modeling-simulation classes that could be improved. Table 2. Questions posed to students about their modeling-simulation ( a ) and traditional classes (b) experience. 1. Motivation a. Do the modeling-simulation classes make the subject more interesting compared to the traditional learning? b. Do the classes make the subject interesting? 2. Content a. Do you find that the topics covered in modeling-simulation classes were adequate and they stimulated your interest in the subject? b. Do you find that the topics covered classes were adequate and they stimulated your interest in the subject? 3. Knowledge a. After the modeling-simulation classes, do you know substantially more about training contents than before? b. After the classes, do you know substantially more about training contents than before? 4. Helpfulness a. Do modeling-simulation classes help you to learn complex topics that would be difficult to understand using traditional methods? b. Do classes help you to learn complex topics that would be difficult to understand? 5. Skills a. Do you think that the modeling-simulation classes were enriching your skills in biorefinery processes? b. Do you think that the classes were enriching your skills in biorefinery processes? 6. Clearness a. Do you think the modeling-simulation classes are clear in presenting the content? b. Do you think the classes are clear in presenting the content? 7. Time a. Do you think the modeling-simulation classes have an adequate duration for developing all proposed contents? b. Do you think the classes have an adequate duration for developing all proposed contents? 8. Enjoyment a. Do you think the modeling-simulation classes are more enjoying classes that traditional classes? b. Do you think the classes are enjoying classes? 9. Recommendation a. Do you recommend using the modeling-simulation classes as a learning method in future editions of course for teaching fermentation-based biorefinery concepts? b. Do you recommend using the traditional learning method in future editions of course for teaching fermentation-based biorefinery concepts? A cognitive test was also distributed to the students including four cognitive questions associated with theoretical explanations of fermentations in well-stirred bioreactors, in order to gauge the students’ improvement in their level of knowledge of the fermentation process and involved mechanisms (see Table 3). Finally, a comparison between the scores of students that perform the computer activities and those of students that did not perform these activities was made. Table 3. Cognitive questions associated with theoretical explanations of fermentations in well-stirred bioreactors. 1. Write the dynamics of the continuous well-stirred bioreactor for the production of bioethanol by the fermentation of lignocellulosic biomass. 2. Write a general set of differential equations describing the waste treatment facility that consists of a completely mixed biodigester and a settler. You can assume no growth and perfect separation occurs in the settler. 3. Determine stationary points and wash-out of a continuous well-stirred bioreactor. 4. Write a general set of differential equations describing the competition between two bacterial species in the batch well-stirred bioreactor. 3. Results and Discussions Questionnaires were assessed after the classes to obtain the students’ level of knowledge of the dynamic modeling of batch, semi-batch, and continuous well-stirred bioreactors used in the
Energies 2020,13, 5772 9 of 16 fermentation process in biorefineries, as well to determine their opinion regarding the type of classes used for the teaching. Table 4shows the results of the questionnaire expressed in percentage of each obtained score for each item, where column a shows results after performing modeling-simulation classes and column b shows results after traditional classes. Moreover, the mean value for each question after each method is showed in Figure 4. A high level of motivation was measured for simulation classes, 88.1% of 84 students found them interesting (on a scale of 1 to 5, with 4 and 5 representing “interesting”), confirming obtained results by other authors, as Serrano-Aroca [ 21 ], and reaching the proposed aims by the authors. Furthermore, 87.1% of students indicated that the topics covered in modeling-simulation classes were adequate and stimulated their interest in the subject. With respect to knowledge, 92.1% of students learned about fermentation by using the modeling-simulation classes. In this sense, results of this question were indicative of achieving the proposed methodology, due to that one of main aims of this study was to improve the training contents of the students in biochemical engineering courses. Therefore, authors consider that the implementation of modeling tools for teaching biorefinery is very useful for students. In addition, 88.1% of the students think that modeling-simulation classes helped them learn complex topics that would be difficult to understand using traditional methods. With regards to skills, 79.2% of students indicated that modeling-simulation classes enriched their skills as biotechnologists/biochemical engineers. With respect to clarity and duration, more than 80% of students found the modeling-simulation classes to be clear and with an appropriate duration. Finally, 86.1% of students think that the modeling-simulation classes were more enjoyable that traditional ones and 58.4% of them would recommend these classes as a learning method in future subjects. Although students thought that the modeling-simulation classes as a learning method helped the students to acquire competences, learn complex topics, and improve their skills in energy processes (questions 3, 4, and 5), from results of Figure 4it is clear that the results were not as expected in question 9, because the score about the recommendation of the students of the modeling-simulation classes as a learning method in future subjects was the lowest obtained (3.71). In this point, new studies are necessary to understand the low score of these questions. Authors think that perhaps the question is not well formulated and students considered not-teaching reasons (as problems in the computers or arrangement of the class) in their answers. Table 4. Answers of students to the questionnaire after performing modeling-simulation classes ( a ) and traditional classes (b). Values are expressed in percentage. 12345 a b a b a b a b a b 1. Motivation 3.96 11.76 1.98 11.76 5.94 35.29 39.60 23.53 48.51 17.65 2. Content 0.99 11.76 2.97 11.76 8.91 41.18 41.58 17.65 45.54 17.65 3. Knowledge 0.00 17.65 0.00 17.65 7.92 35.29 24.75 17.65 67.33 11.76 4. Helpfulness 2.97 23.53 1.98 17.65 6.93 35.29 31.68 11.76 56.44 11.76 5. Skills 1.98 11.76 4.95 23.53 13.86 41.18 32.67 11.76 46.53 11.76 6. Clearness 0.00 29.41 0.99 17.65 18.81 29.41 31.68 11.76 48.51 11.76 7. Time 0.00 23.53 3.96 23.53 15.84 29.41 35.64 11.76 44.55 11.76 8. Enjoyment 0.99 29.41 5.94 23.53 6.93 35.29 35.64 11.76 50.50 0.00 9. Recommendation 1.98 23.53 5.94 23.53 33.66 41.18 35.64 5.88 22.77 5.88
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