Abstract: Predictive methods such as machine learning methods based on neural network technology require large sets of historical training data that are often not available or may not represent the required range of operating scenarios, seasons etc. Accordingly, deployment of machine learning may be impeded or delayed while suitable training data is collected. Further, the characteristics of the plant and its component may change over time due to wear and tear, maintenance events, equipment replacement or upgrades such that the predictive models must be updated (re-trained). Determining an effective schedule for such re-training and the re-training process introduces additional costs, as well as the risk that seasonality and other variables may not be properly captured in the process of re-training these models. Accordingly, it would be beneficial to provide methods and system that mitigate these obstacles with respect to the commercial application of machine-learning and other predictive methods.

Abstract: A vaporization lighter device includes a heat source for producing a flame and directing the same in a horizontal direction. The heat source is perpendicularly aligned with a flame filter via a main body. The flame filter includes an elongated chamber having an open front end, an open second end, and an aperture along the central portion. A non-combustible filter material is positioned within the elongated chamber and spans the central aperture. Ambient air is drawn through the open first end, across the filter material where it absorbs heat, and leaves the second end as super-heated air.