本研究開發一套整合生質柴油各製程之系統。微流體技術雖具備高表面體積比之優勢，但目前現有研究多偏重單一製程之最佳化，缺乏整合反應、萃取與分離之連續式系統。此外，涉及多項物理化學複雜製程，傳統統計模型往往難以捕捉其非線性特徵。鑑於此，本研究提出整合生質柴油製程之晶片系統，並首創「多重類神經網路-基因演算法（Multiple ANN-GA）」方法。
本研究利用軟光刻技術製作聚二甲基矽氧烷（PDMS）微流體晶片。轉酯化區採用流聚焦結構生成液珠。純化區則引入檸檬酸水溶液進行酸洗，並利用毛細管設計達成油水分離。且為解決多變數最佳化問題，本研究分別建構針對「轉酯化反應」與「純化萃取」之獨立類神經網路模型，並透過基因演算法，在滿足生質柴油轉換率與甲醇殘留量之限制下，尋求最佳操作參數。在轉酯化實驗中，當催化劑濃度提升至 2.5 wt% 且油醇流率比為 1 時，反應於 60 秒內即可達近乎完全轉換。透過 MANN-GA 方法，本研究成功驗證兩組最佳化製程條件，其一為在標準規範下，實測轉換率達 95.53% 且甲醇殘留為 0.1961 wt%，其二為高效能條件下，實測轉換率達 98.74% 且甲醇殘留僅 0.1443 wt%，兩者與預測值誤差均低於 4%。此外，針對晶片串聯時因中間產物導致之乳化與逆反應問題，本研究證實將靜置溫度提升至 55°C 可顯著促進殘餘中間產物轉化，有效解決製程整合之瓶頸。本研究不僅證實了微流體技術在整合生質柴油製程之潛力，更建立了一套結合人工智慧之最佳化製程設計方法。

This study develops a system integrating various biodiesel production processes. While microfluidic technology offers high surface-to-volume ratios, existing research has focused on optimizing single processes, lacking a continuous system that effectively integrates reaction, extraction, and separation. Furthermore, given the complex physicochemical processes, traditional statistical models often fail to capture inherent system non-linearities. Consequently, this study proposes an integrated microfluidic chip system for biodiesel production and introduces a novel "Multiple ANN-GA" approach.
Polydimethylsiloxane (PDMS) microfluidic chips were fabricated using soft lithography. A flow-focusing structure was employed in the transesterification zone to generate droplets. For the purification zone, an aqueous citric acid solution was introduced for acid washing, utilizing a capillary design to achieve effective oil-water separation. To address the multi-variable optimization problem, this study constructed independent Artificial Neural Network (ANN) models for the transesterification reaction and purification extraction processes. A Genetic Algorithm (GA) was then applied to determine optimal operating parameters while satisfying constraints regarding biodiesel conversion rates and methanol residues.
Results showed that with a catalyst concentration of 2.5 wt% and an oil-to-alcohol flow ratio of 1, the reaction achieved near-complete conversion within 60 seconds. Utilizing the MANN-GA method, two sets of optimal process conditions were verified. Under standard specification conditions, the measured conversion rate was 95.53% with a methanol residue of 0.1961 wt%. Under high-efficiency conditions, the conversion rate reached 98.74% with a methanol residue of only 0.1443 wt%. Both scenarios demonstrated prediction errors of less than 4%. Furthermore, increasing the settling temperature to 55°C significantly promoted residual intermediate conversion, effectively resolving emulsification and reverse reaction issues during chip cascading. This research confirms the potential of microfluidic technology in integrating biodiesel production and establishes a rigorous optimization methodology combining process design with artificial intelligence.

[1] K. Mainzer, "Interdisciplinarity and innovation dynamics. On convergence of research, technology, economy, and society," Poiesis & Praxis, vol. 7, pp. 275-289, 2011.
[2] E. T. Sayed et al., "Renewable energy and energy storage systems," Energies, vol. 16, no. 3, p. 1415, 2023.
[3] M. Setiyo, D. Yuvenda, and O. D. Samuel, "The Concise latest report on the advantages and disadvantages of pure biodiesel (B100) on engine performance: Literature review and bibliometric analysis," Indonesian Journal of Science and Technology, vol. 6, no. 3, pp. 469-490, 2021.
[4] M. Tuner, "Review and benchmarking of alternative fuels in conventional and advanced engine concepts with emphasis on efficiency, co 2, and regulated emissions," SAE Technical Paper, 0148-7191, 2016.
[5] D. Singh et al., "A comprehensive review on 1st-generation biodiesel feedstock palm oil: production, engine performance, and exhaust emissions," BioEnergy Research, vol. 14, pp. 1-22, 2021.
[6] Z. Dong, Z. Wen, F. Zhao, S. Kuhn, and T. Noël, "Scale-up of micro-and milli-reactors: An overview of strategies, design principles and applications," Chemical Engineering Science: X, vol. 10, p. 100097, 2021.
[7] T. Han, L. Zhang, H. Xu, and J. Xuan, "Factory-on-chip: Modularised microfluidic reactors for continuous mass production of functional materials," Chemical Engineering Journal, vol. 326, pp. 765-773, 2017.
[8] A. D. Chintagunta et al., "Biodiesel production from lignocellulosic biomass using oleaginous microbes: prospects for integrated biofuel production," Frontiers in Microbiology, vol. 12, p. 658284, 2021.
[9] M. Rajendra, P. C. Jena, and H. Raheman, "Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA," Fuel, vol. 88, no. 5, pp. 868-875, 2009.
[10] M. Ramos, A. P. S. Dias, J. F. Puna, J. Gomes, and J. C. Bordado, "Biodiesel production processes and sustainable raw materials," Energies,vol. 12, no. 23, p. 4408, 2019.
[11] A. A. Babadi et al., "Emerging technologies for biodiesel production: processes, challenges, and opportunities," Biomass and Bioenergy, vol. 163, p. 106521, 2022.
[12] M. C. Manique, L. V. Lacerda, A. K. Alves, and C. P. Bergmann, "Synthesis of hydro-sodalite as a heterogeneous catalyst for reaction kinetics of soybean oil trans-esterification," Journal of Material Sciences & Engineering [recurso eletrônico].[Sl]. Vol. 7, no. 6 (2018), 6 p., 2018.
[13] P. Maheshwari et al., "A review on latest trends in cleaner biodiesel production: Role of feedstock, production methods, and catalysts," Journal of Cleaner Production, vol. 355, p. 131588, 2022.
[14] F. Toldrá-Reig, L. Mora, and F. Toldrá, "Trends in biodiesel production from animal fat waste," Applied Sciences, vol. 10, no. 10, p. 3644, 2020.
[15] B. Thangaraj, P. R. Solomon, B. Muniyandi, S. Ranganathan, and L. Lin, "Catalysis in biodiesel production—a review," Clean Energy, vol. 3, no. 1, pp. 2-23, 2019.
[16] V. Mandari and S. K. Devarai, "Biodiesel production using homogeneous, heterogeneous, and enzyme catalysts via transesterification and esterification reactions: A critical review," BioEnergy Research, vol. 15, no. 2, pp. 935-961, 2022.
[17] X. Zhang, M. Fevre, G. O. Jones, and R. M. Waymouth, "Catalysis as an enabling science for sustainable polymers," Chemical reviews, vol. 118, no. 2, pp. 839-885, 2018.
[18] E. Santacesaria, M. Di Serio, R. Tesser, R. Turco, M. Tortorelli, and V. Russo, "Biodiesel process intensification in a very simple microchannel device," Chemical Engineering and Processing: Process Intensification, vol. 52, pp. 47-54, 2012.
[19] F. Schönfeld, V. Hessel, and C. Hofmann, "An optimised split-and-recombine micro-mixer with uniform ‘chaotic’mixing," Lab on a Chip, vol. 4, no. 1, pp. 65-69, 2004.
[20] J. Berthier, V.-M. Tran, F. Mittler, and N. Sarrut, "The physics of a coflow micro-extractor: interface stability and optimal extraction length," Sensors and Actuators A: Physical, vol. 149, no. 1, pp. 56-64, 2009.
[21] S. Ookawara, T. Ishikawa, and K. Ogawa, "Applicability of a miniaturized micro‐separator/classifier to oil‐water separation," Chemical Engineering & Technology: Industrial Chemistry‐Plant Equipment‐Process Engineering‐Biotechnology, vol. 30, no. 3, pp. 316-321, 2007.
[22] L. D. Garza-García et al., "A biopharmaceutical plant on a chip: continuous micro-devices for the production of monoclonal antibodies," Lab on a Chip, vol. 13, no. 7, pp. 1243-1246, 2013.
[23] R. Bhoi et al., "Transesterification of sunflower oil in microreactors," International Journal of Chemical Reactor Engineering, vol. 12, no. 1, pp. 47-62, 2014.
[24] T. Xie, L. Zhang, and N. Xu, "Biodiesel synthesis in microreactors," 2012.
[25] S. Gambhire, N. Patel, G. Gambhire, and S. Kale, "A review on different micromixers and its micromixing within microchannel," International Journal of Current Engineering and Technology, vol. 4, no. 4, pp. 409-413, 2016.
[26] L. Capretto, W. Cheng, M. Hill, and X. Zhang, "Micromixing within microfluidic devices," Microfluidics: technologies and applications, pp. 27-68, 2011.
[27] C. Liu, Y. Li, and B.-F. Liu, "Micromixers and their applications in kinetic analysis of biochemical reactions," Talanta, vol. 205, p. 120136, 2019.
[28] G. Cai, L. Xue, H. Zhang, and J. Lin, "A review on micromixers," Micromachines, vol. 8, no. 9, p. 274, 2017.
[29] C.-Y. Lee, W.-T. Wang, C.-C. Liu, and L.-M. Fu, "Passive mixers in microfluidic systems: A review," Chemical Engineering Journal, vol. 288, pp. 146-160, 2016.
[30] A. Gholami, F. Pourfayaz, A. Hajinezhad, and M. Mohadesi, "Biodiesel production from Norouzak (Salvia leriifolia) oil using choline hydroxide catalyst in a microchannel reactor," Renewable energy, vol. 136, pp. 993-1001, 2019.
[31] P. Sun, B. Wang, J. Yao, L. Zhang, and N. Xu, "Fast synthesis of biodiesel at high throughput in microstructured reactors," Industrial & engineering chemistry research, vol. 49, no. 3, pp. 1259-1264, 2010.
[32] Z. Wen, X. Yu, S.-T. Tu, J. Yan, and E. Dahlquist, "Intensification of biodiesel synthesis using zigzag micro-channel reactors," Bioresource technology, vol. 100, no. 12, pp. 3054-3060, 2009.
[33] E. L. Martinez Arias, P. Fazzio Martins, A. L. Jardini Munhoz, L. Gutierrez-Rivera, and R. Maciel Filho, "Continuous synthesis and in situ monitoring of biodiesel production in different microfluidic devices," Industrial & Engineering Chemistry Research, vol. 51, no. 33, pp. 10755-10767, 2012.
[34] A. Boukhalkhal et al., "A continuous biodiesel production process using a chaotic mixer-reactor," Waste and Biomass Valorization, vol. 11, pp. 6159-6168, 2020.
[35] H. S. Santana, J. L. Silva Jr, D. S. Tortola, and O. P. Taranto, "Transesterification of sunflower oil in microchannels with circular obstructions," Chinese journal of chemical engineering, vol. 26, no. 4, pp. 852-863, 2018.
[36] C. Xu and T. Xie, "Review of microfluidic liquid–liquid extractors," Industrial & Engineering Chemistry Research, vol. 56, no. 27, pp. 7593-7622, 2017.
[37] M. Sun, W.-B. Du, and Q. Fang, "Microfluidic liquid–liquid extraction system based on stopped-flow technique and liquid core waveguide capillary," Talanta, vol. 70, no. 2, pp. 392-396, 2006.
[38] T. Xie, M. Chen, C. Xu, and J. Chen, "High-throughput extraction and separation of Ce (III) and Pr (III) using a chaotic advection microextractor," Chemical Engineering Journal, vol. 356, pp. 382-392, 2019.
[39] C.-C. Hong, J.-W. Choi, and C. H. Ahn, "A novel in-plane passive microfluidic mixer with modified Tesla structures," Lab on a Chip, vol. 4, no. 2, pp. 109-113, 2004.
[40] J. Burns and C. Ramshaw, "The intensification of rapid reactions in multiphase systems using slug flow in capillaries," Lab on a Chip,vol. 1, no. 1, pp. 10-15, 2001.
[41] W. Lan, S. Jing, S. Li, and G. Luo, "Hydrodynamics and mass transfer in a countercurrent multistage microextraction system," Industrial & Engineering Chemistry Research, vol. 55, no. 20, pp. 6006-6017, 2016.
[42] T. Bu, D. Mesa, and P. R. Brito-Parada, "Design strategies for miniaturised liquid–liquid separators—A critical review," Chemical Engineering Journal, vol. 495, p. 153036, 2024.
[43] F. Scheiff, M. Mendorf, D. Agar, N. Reis, and M. Mackley, "The separation of immiscible liquid slugs within plastic microchannels using a metallic hydrophilic sidestream," Lab on a Chip, vol. 11, no. 6, pp. 1022-1029, 2011.
[44] S. Kamrani and A. Mohammadi, "Controlling the microscale separation of immiscible liquids using geometry: A computational fluid dynamics study," Chemical Engineering Science, vol. 220, p. 115625, 2020.
[45] T. Maruyama et al., "Intermittent partition walls promote solvent extraction of metal ions in a microfluidic device," Analyst, vol. 129, no. 11, pp. 1008-1013, 2004.
[46] A. Ładosz and P. Rudolf von Rohr, "Design rules for microscale capillary phase separators," Microfluidics and Nanofluidics, vol. 21, pp. 1-16, 2017.
[47] P. Baptista, P. Felizardo, J. C. Menezes, and M. J. N. Correia, "Multivariate near infrared spectroscopy models for predicting the methyl esters content in biodiesel," Analytica chimica acta, vol. 607, no. 2, pp. 153-159, 2008.
[48] E. M. Paiva, J. J. R. Rohwedder, C. Pasquini, M. F. Pimentel, and C. F. Pereira, "Quantification of biodiesel and adulteration with vegetable oils in diesel/biodiesel blends using portable near-infrared spectrometer," Fuel, vol. 160, pp. 57-63, 2015.
[49] K. Singh, S. P. Kumar, and B. Blümich, "Monitoring the mechanism and kinetics of a transesterification reaction for the biodiesel production with low field 1H NMR spectroscopy," Fuel, vol. 243, pp. 192-201, 2019.
[50] G. G. Shimamoto and M. Tubino, "Quantification of methanol in biodiesel through 1H nuclear magnetic resonance spectroscopy," Fuel,vol. 175, pp. 99-104, 2016.
[51] H. S. Pali, A. Sharma, N. Kumar, and Y. Singh, "Biodiesel yield and properties optimization from Kusum oil by RSM," Fuel, vol. 291, p. 120218, 2021.
[52] K. S. Moreira et al., "Optimization of the production of enzymatic biodiesel from residual babassu oil (Orbignya sp.) via RSM," Catalysts, vol. 10, no. 4, p. 414, 2020.
[53] J. C. Oraegbunam, N. B. Ishola, B. A. Sotunde, L. M. Latinwo, and E. Betiku, "Sandbox oil biodiesel production modeling and optimization with neural networks and genetic algorithm," Green Technologies and Sustainability, vol. 1, no. 1, p. 100007, 2023.
[54] S. Sivamani, S. Selvakumar, K. Rajendran, and S. Muthusamy, "Artificial neural network–genetic algorithm-based optimization of biodiesel production from Simarouba glauca," Biofuels, vol. 10, no. 3, pp. 393-401, 2019.
[55] 王俊傑, "基於類神經網路及基因演算法於微反應器生質柴油產製之最佳化設計," 2024.
[56] 蔡依璇, "基於類神經網路及基因演算法之生質柴油純化分離微流元件最佳化設計," 2023.
[57] MicroChem. "SU-8 2000 Permanent Epoxy Negative Photoresist PROCESSING GUIDELINES FOR: SU-8 2025, SU-8 2035, SU-8 2050 and SU-8 2075." www.atgc.co.jp (accessed.
[58] S. Yeh, Y. Huang, C. Cheng, C. Cheng, and J. Yang, "Development of a millimetrically scaled biodiesel transesterification device that relies on droplet-based co-axial fluidics," Scientific reports, vol. 6, no. 1, p. 29288, 2016.
[59] G. Knothe, "Monitoring a progressing transesterification reaction by fiber‐optic near infrared spectroscopy with correlation to 1H nuclear magnetic resonance spectroscopy," Journal of the American Oil Chemists' Society, vol. 77, no. 5, pp. 489-493, 2000.
[60] A. Gholamy, V. Kreinovich, and O. Kosheleva, "Why 70/30 or 80/20 relation between training and testing sets: A pedagogical explanation," 2018.
[61] G. Moradi, S. Dehghani, F. Khosravian, and A. Arjmandzadeh, "The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield," Renewable energy, vol. 50, pp. 915-920, 2013.