{'fraction_non_alphanumeric': 0.012391573729863693, 'fraction_numerical': 0.0, 'mean_word_length': 5.263565891472868, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A real-time soft measurement method based on the furnace system parameters of the forecast model

The complex and changing parameters in the furnace are difficult for online measurement in the heating process. To solve this problem, a soft measurement method was explored using the prediction model that is still worthy of being promoted. This paper gets started by constructing a forecast model for the furnace controller, and then the augmented recursive least squares algorithm with an exponential forgetting factor was introduced to perform an unbiased estimate on the parameters. Simulation results show that such a real-time soft measurement approach works well for tracking the parameter fluctuations. Its good robustness provides a reference method for on-line measurement of the furnace parameters.