Using neural network to establish manufacture production performance forecasting in IoT environment

Liu, Zhifang

doi:10.1007/s11227-021-04210-8

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…Catboost Regression represents a relatively recent and purportedly potent machine learning algorithm, offering several advantages [21]. In essence, machine learning algorithms are commonly utilized to discern intricate patterns within extensive datasets, enabling predictions of future behaviors.…”

Section: Catboost Algorithm Applicationmentioning

confidence: 99%

Machine Learning for Energy-Efficient Fluid Bed Dryer Pharmaceutical Machines

Barriga,

Romero,

Hassan

2023

Electronics

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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.

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Section: Catboost Algorithm Applicationmentioning

confidence: 99%