[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120121-en":3,"doc-seo-120121-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120121,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning for Energy-Efficient Fluid Bed Dryer Pharmaceutical Machines - EDA and Catboost Model","Pharmaceutical manufacturing faces economic pressure from healthcare cost containment and evolving regulations, driving laboratories to extend equipment lifecycles, especially fluid bed dryers central to drug production. Older dryers often lack sensors enabling real-time temperature optimization and rely on fixed-time deterministic operation managed by operators. This research combines exploration data analysis with a Catboost machine-learning model to analyze and improve large-scale drug production, reducing preheating phase time and energy use by about half on average.","electronics   \nArticle  \nMachine Learning for Energy-Efficient Fluid Bed Dryer Pharmaceutical Machines  \nRoberto Barriga 1, Miquel Romero 2 and Houcine Hassan 1, *  \nCitation: Barriga, R.; Romero, M.; Hassan, H. Machine Learning for Energy-Efficient Fluid Bed Dryer Pharmaceutical Machines. Electronics 2023, 12, 4325. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/electronics12204325](10.3390/electronics12204325)  \nAcademic Editor: Adel M. Sharaf  \nReceived: 20 September 2023  \nRevised: 11 October 2023  \nAccepted: 17 October 2023  \nPublished: 18 October 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Departamento de Inform¡tica de Sistemas y Computadores, Universitat Polit±cnica de Val±ncia, Camino de Vera, nº14, 46022 Valencia, Spain  \n2 Industrias Farmac²uticas Almirall, Ctra. N-II, km. 593, 08740 Sant Andreu de la Barca, Spain  \n* Correspondence: husein@disca.upv.es  \nAbstract: The pharmaceutical industry is facing significant economic challenges due to measures aimed at containing healthcare costs and evolving healthcare regulations. In this context, pharmaceutical laboratories seek to extend the lifespan of their machinery, particularly fluid bed dryers, which play a crucial role in the drug production process. Older fluid bed dryers, lacking advanced sensors for real-time temperature optimization, rely on fixed-time deterministic approaches controlled by operators. To address these limitations, a groundbreaking approach taking into account Exploration Data Analysis (EDA) and a Catboost machine-learning model is presented. This research aims to analyze and enhance a drug production process on a large scale, showcasing how AI algorithms can revolutionize the manufacturing industry. The Catboost model effectively reduces preheating phase time, resulting in significant energy savings. By continuously monitoring critical parameters, a paradigm shift from the conventional fixed-time models is achieved. It has been shown that the model is able to predict on average a reduction of 50.45% of the preheating process duration and up to 59.68% in some cases. Likewise, the energy consumption of the fluid bed dryer for the preheating process could be reduced on average by 50.48% and up to 59.76%, which would result on average in around 3.120 kWh energy consumption savings per year.  \nKeywords: energy consumption; IoT-based power control systems; machine learning; optimization using sensor data; predictive control; pharmaceutical technology; process modeling; exploratory data analysis  \n1. Introduction  \nThe entire pharmaceutical manufacturing process comprises multiple stages, including dispensing, granulation, drying, compression, and coating [1], as depicted in the diagram below in Figure 1.  \nFluid bed drying technology is widely employed in pharmaceutical manufacturing due to its high efficiency in drying granules obtained through wet granulation [2] . However, the primary challenge associated with using a fluid bed dryer lies in the time and energy it consumes to complete the process. The drying process entails three phases: (i) preheating the machine without introducing any product,(ii) drying the product, and (iii) cooling the machine for product cooling. Costs are incurred in all three phases, encompassing the time taken by the machines and the energy required for heating and air circulation. Additionally, the budget is impacted by the number of operators involved in handling the machine [3] . The fluid bed drying of wet granules obtained through high shear granulation involves a combination of moisture diffusion from the solid material, facilitated by hot air, and the entrainment of this moisture through f","cbCaijlrCJsGebkm","https://ap.wps.com/l/cbCaijlrCJsGebkm","pdf",2571707,1,16,"English","en",105,"# Introduction\n## Pharmaceutical manufacturing workflow\n## Fluid bed drying phases and constraints\n## Need for sensor-based optimization","[{\"question\":\"Why do pharmaceutical laboratories need more energy-efficient fluid bed drying?\",\"answer\":\"They face economic challenges from healthcare cost control and regulations, and fluid bed dryers are costly to replace, so improving energy use and extending equipment lifecycles is critical.\"},{\"question\":\"What limitation exists in older fluid bed dryers?\",\"answer\":\"Many older units lack advanced sensors for real-time temperature optimization, so they depend on fixed-time deterministic approaches controlled by operators.\"},{\"question\":\"How does the proposed method improve performance?\",\"answer\":\"It uses exploration data analysis and a Catboost machine-learning model to monitor key parameters and reduce preheating duration, leading to substantial energy savings and predictive reduction in time.\"}]","Machine Learning for Energy-Efficient Fluid Bed Dryer Pharmaceutical Machines - 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