Design of Tools for Visualizing Thermodynamic Concepts in Steam Power Plant Trainer Processes with Web-Based Exploratory Data Analysis (EDA)

Arwizet Karudin - Universitas Negeri Padang, Padang, Indonesia
Desmarita Leni - Universitas Muhammadiyah Sumatera Barat, Padang, Indonesia
Remon Lapisa - Universitas Negeri Padang, Padang, Indonesia
Yuda Perdana Kusuma - Faculty of Agricultural Technology, Andalas University, Indonesia
Muhammad Rabiu Abbas - Federal University of Transportation, Daura 824101, Katsina, Nigeria


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3.2139

Abstract


Thermodynamics is considered one of the most complex and challenging subjects for many students. This is primarily due to comprehending abstract concepts such as entropy, enthalpy, and energy flow, which involve complex mathematical equations and are rarely accompanied by tangible visualizations. This research aims to design, develop, and test a data-based visualization tool for thermodynamics testing results. This study collected and processed data from thermodynamics testing and simulations, such as the mini-steam power plant trainer used as a teaching aid in thermodynamics education, as the foundation for designing a data-based visualization tool for thermodynamics concepts. The visualization tool was created using the Python programming language integrated with the web-based Streamlit framework. The designed visualization tool encompasses various features, including automated data reporting, visualization of variable correlations using correlation heatmaps, Sankey diagrams for visualizing energy flow, and the capability to predict electrical output using machine learning integrated with three different machine learning algorithms. The visualization tool was evaluated by thermodynamics experts using a Likert scale. Based on the results obtained, the experts gave an average score of 4 in the information accuracy aspect in the good category. This shows that the information displayed in this visualization tool is by thermodynamics learning at Padang State University. In the visualization aspect, experts gave an average score of 4.25, which is in the Good and Very Good range. In alignment with the education aspect, experts gave an average score of 3.75, which is close to the good category. This shows that this aspect is considered suitable for studying thermodynamics, although shortcomings still need to be corrected. Experts gave a relatively high assessment of the Ease-of-Use aspect, with an average score of 4.5, with a range of Good and Very Good. This enables students to better understand complex patterns, cause-and-effect relationships, and parameter changes within thermodynamics concepts.


Keywords


Design , Visualization, Thermodynamics , Exploratory Data Analysis

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References


E. Cueto and F. Chinesta, “Thermodynamics of Learning Physical Phenomena,” Arch. Comput. Methods Eng., vol. 30, no. 8, pp. 4653–4666, Nov. 2023, doi: 10.1007/s11831-023-09954-5.

J. Zhang, H. Cho, and P. J. Mago, “Improving Student Learning of Energy Systems through Computational Tool Development Process in Engineering Courses,” Sustainability, vol. 13, no. 2, p. 884, Jan. 2021, doi: 10.3390/su13020884.

S. A. Finkenstaedt-Quinn et al., “Capturing student conceptions of thermodynamics and kinetics using writing,” Chem. Educ. Res. Pract., vol. 21, no. 3, pp. 922–939, 2020, doi: 10.1039/C9RP00292H.

J.-C. De Hemptinne et al., “A View on the Future of Applied Thermodynamics,” Ind. Eng. Chem. Res., vol. 61, no. 39, pp. 14664–14680, Oct. 2022, doi: 10.1021/acs.iecr.2c01906.

M. J. Brundage and C. Singh, “Development and validation of a conceptual multiple-choice survey instrument to assess student understanding of introductory thermodynamics,” Phys. Rev. Phys. Educ. Res., vol. 19, no. 2, p. 020112, Aug. 2023, doi: 10.1103/PhysRevPhysEducRes.19.020112.

S. A. Chaturvedi, T. Abdel-Salam, and R. Arora, “A web-based simulation and visualization module for an undergraduate thermodynamics course,” Comput. Educ. J., vol. 5, no. 2, pp. 58–67, 2014.

M. R. D. Biasi, G. E. Valencia, and L. G. Obregon, “A New Educational Thermodynamic Software to Promote Critical Thinking in Youth Engineering Students,” Sustainability, vol. 12, no. 1, p. 110, Dec. 2019, doi: 10.3390/su12010110.

T. Milo and A. Somech, “Automating Exploratory Data Analysis via Machine Learning: An Overview,” in Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, Portland OR USA: ACM, Jun. 2020, pp. 2617–2622. doi: 10.1145/3318464.3383126.

Dr. T. H. S. Tariq and P. S. Aithal, “Visualization and Explorative Data Analysis,” SSRN Electron. J., 2023, doi: 10.2139/ssrn.4400256.

P. Ma, R. Ding, S. Han, and D. Zhang, “MetaInsight: Automatic Discovery of Structured Knowledge for Exploratory Data Analysis,” in Proceedings of the 2021 International Conference on Management of Data, Virtual Event China: ACM, Jun. 2021, pp. 1262–1274. doi: 10.1145/3448016.3457267.

J. Hullman and A. Gelman, “Designing for Interactive Exploratory Data Analysis Requires Theories of Graphical Inference,” Harv. Data Sci. Rev., Jul. 2021, doi: 10.1162/99608f92.3ab8a587.

M. Ma, J. Yang, P. Wang, W. Liu, and J. Zhang, “Light-Weight and Scalable Hierarchical-MVC Architecture for Cloud Web Applications,” in 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), Paris, France: IEEE, Jun. 2019, pp. 40–45. doi: 10.1109/CSCloud/EdgeCom.2019.00017.

D. Leni, F. Earnestly, R. Sumiati, A. Adriansyah, and Y. P. Kusuma, “Evaluasi sifat mekanik baja paduan rendah bedasarkan komposisi kimia dan suhu perlakuan panas menggunakan teknik exploratory data analysis (EDA),” Din. Tek. Mesin, vol. 13, no. 1, p. 74, Apr. 2023, doi: 10.29303/dtm.v13i1.624.

J. M. Nápoles-Duarte, A. Biswas, M. I. Parker, J. P. Palomares-Baez, M. A. Chávez-Rojo, and L. M. Rodríguez-Valdez, “Stmol: A component for building interactive molecular visualizations within streamlit web-applications,” Front. Mol. Biosci., vol. 9, p. 990846, Sep. 2022, doi: 10.3389/fmolb.2022.990846.

J. Peng et al., “DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python,” in Proceedings of the 2021 International Conference on Management of Data, Virtual Event China: ACM, Jun. 2021, pp. 2271–2280. doi: 10.1145/3448016.3457330.

C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Front. Energy Res., vol. 9, p. 652801, Mar. 2021, doi: 10.3389/fenrg.2021.652801.

M. S. Smitha Rao, M. Pallavi, and N. Geetha, “Conceptual Machine Learning Framework for Initial Data Analysis,” in Computing and Network Sustainability, vol. 75, S.-L. Peng, N. Dey, and M. Bundele, Eds., in Lecture Notes in Networks and Systems, vol. 75. , Singapore: Springer Singapore, 2019, pp. 51–59. doi: 10.1007/978-981-13-7150-9_6.

S. learn, “Scikit-learn (BSD License).” [Online]. Available: https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_3_0.html

K. Keras, “TensorFlow Core,” TensorFlow 2.12. [Online]. Available: https://blog.tensorflow.org/2023/03/whats-new-in-tensorflow-212.html

R. Siddiqui et al., “Power Prediction of Combined Cycle Power Plant (CCPP) Using Machine Learning Algorithm-Based Paradigm,” Wirel. Commun. Mob. Comput., vol. 2021, pp. 1–13, Dec. 2021, doi: 10.1155/2021/9966395.

D. Chary, “Prediction of Full Load Electrical Power Output of a Base Load Operated Combined Cycle Power Plant Using Machine Learning Methods,” SSRN Electron. J., 2021, doi: 10.2139/ssrn.3945086.

N. S. Santarisi and S. S. Faouri, “Prediction of combined cycle power plant electrical output power using machine learning regression algorithms,” East.-Eur. J. Enterp. Technol., vol. 6, no. 8 (114), pp. 16–26, Dec. 2021, doi: 10.15587/1729-4061.2021.245663.

D. Leni, Y. P. Kusuma, M. Muchlisinalahuddin, F. Earnerstly, R. Muharni, and R. Sumiati, “PERANCANGAN METODE MACHINE LEARNING BERBASIS WEB UNTUK PREDIKSI SIFAT MEKANIK ALUMINIUM,” J. Rekayasa Mesin, vol. 14, no. 2, pp. 611–626, Aug. 2023, doi: 10.21776/jrm.v14i2.1370.

D. Leni, A. Karudin, M. R. Abbas, J. K. Sharma, and A. Adriansyah, “Optimizing stainless steel tensile strength analysis: through data exploration and machine learning design with Streamlit,” EUREKA Phys. Eng., no. 5, pp. 73–88, Sep. 2024, doi: 10.21303/2461-4262.2024.003296.

M. D. B. Castro and G. M. Tumibay, “A literature review: efficacy of online learning courses for higher education institution using meta-analysis,” Educ. Inf. Technol., vol. 26, no. 2, pp. 1367–1385, Mar. 2021, doi: 10.1007/s10639-019-10027-z.

A. Stanciulescu, F. Castronovo, and J. Oliver, “Assessing the impact of visualization media on engagement in an active learning environment,” Int. J. Math. Educ. Sci. Technol., pp. 1–21, Mar. 2022, doi: 10.1080/0020739X.2022.2044530.

B. Aquilani, M. Piccarozzi, T. Abbate, and A. Codini, “The Role of Open Innovation and Value Co-creation in the Challenging Transition from Industry 4.0 to Society 5.0: Toward a Theoretical Framework,” Sustainability, vol. 12, no. 21, p. 8943, Oct. 2020, doi: 10.3390/su12218943.

S. Babicki et al., “Heatmapper: web-enabled heat mapping for all,” Nucleic Acids Res., vol. 44, no. W1, pp. W147–W153, Jul. 2016, doi: 10.1093/nar/gkw419.

Z. Gu, “Complex heatmap visualization,” iMeta, vol. 1, no. 3, p. e43, Sep. 2022, doi: 10.1002/imt2.43.

A. J. Bishara and J. B. Hittner, “Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches.,” Psychol. Methods, vol. 17, no. 3, pp. 399–417, Sep. 2012, doi: 10.1037/a0028087.

A. Mambro, F. Congiu, and E. Galloni, “Influence of stage design parameters on ventilation power produced by steam turbine last stage blades during low load operation,” Therm. Sci. Eng. Prog., vol. 28, p. 101054, Feb. 2022, doi: 10.1016/j.tsep.2021.101054.

A. Patil et al., “Two-phase operation of a Terry steam turbine using air and water mixtures as working fluids,” Appl. Therm. Eng., vol. 165, p. 114567, Jan. 2020, doi: 10.1016/j.applthermaleng.2019.114567.

A. Joseph Omosanya, E. Titilayo Akinlabi, and J. Olusegun Okeniyi, “Overview for Improving Steam Turbine Power Generation Efficiency,” J. Phys. Conf. Ser., vol. 1378, no. 3, p. 032040, Dec. 2019, doi: 10.1088/1742-6596/1378/3/032040.

S. Ranganathan, M. Gribskov, K. Nakai, and C. Schönbach, Eds., Encyclopedia of bioinformatics and computational biology. Amsterdam Boston Heidelberg London New York Oxford Paris San Diego San Francisco Singapore Sydney Tokyo: Elsevier, 2019.

A. Khalyasmaa et al., “Prediction of Solar Power Generation Based on Random Forest Regressor Model,” in 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), Novosibirsk, Russia: IEEE, Oct. 2019, pp. 0780–0785. doi: 10.1109/SIBIRCON48586.2019.8958063.

C. González, J. Mira‐McWilliams, and I. Juárez, “Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, Bagging and Random Forests,” IET Gener. Transm. Distrib., vol. 9, no. 11, pp. 1120–1128, Aug. 2015, doi: 10.1049/iet-gtd.2014.0655.

M. W. Ahmad, J. Reynolds, and Y. Rezgui, “Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees,” J. Clean. Prod., vol. 203, pp. 810–821, Dec. 2018, doi: 10.1016/j.jclepro.2018.08.207.

V. Veeramsetty, K. R. Reddy, M. Santhosh, A. Mohnot, and G. Singal, “Short-term electric power load forecasting using random forest and gated recurrent unit,” Electr. Eng., vol. 104, no. 1, pp. 307–329, Feb. 2022, doi: 10.1007/s00202-021-01376-5.

J. Rosenberg and M. Lawson, “An Investigation of Students’ Use of a Computational Science Simulation in an Online High School Physics Class,” Educ. Sci., vol. 9, no. 1, p. 49, Mar. 2019, doi: 10.3390/educsci9010049.

J. G. Acevedo, G. Valencia Ochoa, and L. G. Obregon, “Development of a new educational package based on e-learning to study engineering thermodynamics process: combustion, energy and entropy analysis,” Heliyon, vol. 6, no. 6, p. e04269, Jun. 2020, doi: 10.1016/j.heliyon.2020.e04269.

W. Vallejo, C. Díaz-Uribe, and C. Fajardo, “Google Colab and Virtual Simulations: Practical e-Learning Tools to Support the Teaching of Thermodynamics and to Introduce Coding to Students,” ACS Omega, vol. 7, no. 8, pp. 7421–7429, Mar. 2022, doi: 10.1021/acsomega.2c00362.

V. Kumar, B. Pandya, and V. Matawala, “Thermodynamic studies and parametric effects on exergetic performance of a steam power plant,” Int. J. Ambient Energy, vol. 40, no. 1, pp. 1–11, Jan. 2019, doi: 10.1080/01430750.2017.1354326.

B. Rudiyanto et al., “Energy and Exergy Analysis of Steam Power Plant in Paiton, Indonesia,” IOP Conf. Ser. Earth Environ. Sci., vol. 268, no. 1, p. 012091, Jun. 2019, doi: 10.1088/1755-1315/268/1/012091.

K. Munn, S. Goh, M. Basson, and D. Thorpe, “Asset management competency requirements in Australian local government: a systematic literature review,” Australas. J. Eng. Educ., vol. 26, no. 2, pp. 167–200, Jul. 2021, doi: 10.1080/22054952.2021.1934262.

J. Ding et al., “Machine learning for molecular thermodynamics,” Chin. J. Chem. Eng., vol. 31, pp. 227–239, Mar. 2021, doi: 10.1016/j.cjche.2020.10.044.

S. S. Funai and D. Giataganas, “Thermodynamics and feature extraction by machine learning,” Phys. Rev. Res., vol. 2, no. 3, p. 033415, Sep. 2020, doi: 10.1103/PhysRevResearch.2.033415.

F. Jirasek et al., “Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion,” J. Phys. Chem. Lett., vol. 11, no. 3, pp. 981–985, Feb. 2020, doi: 10.1021/acs.jpclett.9b03657.

S. Jafarinejad, L. E. Beckingham, M. Kathe, and K. Henderson, “The Renewable Energy (RE) Industry Workforce Needs: RE Simulation and Analysis Tools Teaching as an Effective Way to Enhance Undergraduate Engineering Students’ Learning,” Sustainability, vol. 13, no. 21, p. 11727, Oct. 2021, doi: 10.3390/su132111727.

S. Mahmoudi Herris et al., “Identification and Assessment of the Effective Factors on the Occurrence of the Environmental Events Caused by the Construction and Operation of Gas and Steam Power Plants (Case Study: MAPNA Group(,” J. Occup. Hyg. Eng., vol. 6, no. 4, pp. 10–17, Feb. 2020, doi: 10.52547/johe.6.4.10.

R. Alizadeh Kheneslu, A. Jahangiri, and M. Ameri, “Interaction effects of natural draft dry cooling tower (NDDCT) performance and 4E (energy, exergy, economic and environmental) analysis of steam power plant under different climatic conditions,” Sustain. Energy Technol. Assess., vol. 37, p. 100599, Feb. 2020, doi: 10.1016/j.seta.2019.100599.

A. Yousif, N. Drou, J. Rowe, M. Khalfan, and K. C. Gunsalus, “NASQAR: a web-based platform for high-throughput sequencing data analysis and visualization,” BMC Bioinformatics, vol. 21, no. 1, p. 267, Dec. 2020, doi: 10.1186/s12859-020-03577-4.

S. H. Chae, Y. Kim, K.-S. Lee, and H.-S. Park, “Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study,” JMIR MHealth UHealth, vol. 8, no. 7, p. e17216, Jul. 2020, doi: 10.2196/17216.

A. Weidlich, G. Gust, and M. Schäfer, “Conference on Energy Informatics:,” Energy Inform., vol. 4, no. S4, pp. 26, s42162-021-00184–2, Sep. 2021, doi: 10.1186/s42162-021-00184-2.

C. Bordin, S. Mishra, A. Safari, and F. Eliassen, “Educating the energy informatics specialist: opportunities and challenges in light of research and industrial trends,” SN Appl. Sci., vol. 3, no. 6, p. 674, Jun. 2021, doi: 10.1007/s42452-021-04610-8.

P. Himthani, G. P. Dubey, B. M. Sharma, and A. Taneja, “Big Data Privacy and Challenges for Machine Learning,” in 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India: IEEE, Oct. 2020, pp. 707–713. doi: 10.1109/I-SMAC49090.2020.9243527.

S. Koyejo and Neural Information Processing Systems Foundation, Eds., 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022. in Advances in neural information processing systems, no. 35. Red Hook, NY: Curran Associates, Inc, 2023.

F. Strieth-Kalthoff, F. Sandfort, M. H. S. Segler, and F. Glorius, “Machine learning the ropes: principles, applications and directions in synthetic chemistry,” Chem. Soc. Rev., vol. 49, no. 17, pp. 6154–6168, 2020, doi: 10.1039/C9CS00786E.

T. Xu, G. Coco, and M. Neale, “A predictive model of recreational water quality based on adaptive synthetic sampling algorithms and machine learning,” Water Res., vol. 177, p. 115788, Jun. 2020, doi: 10.1016/j.watres.2020.115788.

S. I. Nikolenko, Synthetic data for deep learning. in Springer optimization and its applications, no. volume 174. Cham: Springer, 2021. doi: 10.1007/978-3-030-75178-4.

D. Leni, D. S. Kesuma, Maimuzar, Haris, and S. Afriyani, “Prediction of Mechanical Properties of Austenitic Stainless Steels with the Use of Synthetic Data via Generative Adversarial Networks,” in The 7th Mechanical Engineering, Science and Technology International Conference, MDPI, Feb. 2024, p. 4. doi: 10.3390/engproc2024063004.