Monitoring of cane sugar crystallization process by multiscale time-series analysis

Romero-Bustamante, Jorge A.; Velázquez-Camilo, Oscar; García-Hernández, A.; Rivera, Victor M.; Hernández‐Martínez, Eliseo

doi:10.1016/j.chaos.2022.111848

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…This procedure is quite basic and straightforward. It is commonly utilized in time series studies of water quality [14]. Wavelet analysis is the most commonly used frequency domain analysis approach [15].…”

Section: Introductionmentioning

confidence: 99%

Nutrients and Environmental Factors Cross Wavelet Analysis of River Yi in East China: A Multi-Scale Approach

Wang

Song

et al. 2022

IJERPH

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The accumulation of nutrients in rivers is a major cause of eutrophication, and the change in nutrient content is affected by a variety of factors. Taking the River Yi as an example, this study used wavelet analysis tools to examine the periodic changes in nutrients and environmental factors, as well as the relationship between nutrients and environmental factors. The results revealed that total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH4+–N) exhibit multiscale oscillation features, with the dominating periods of 16–17, 26, and 57–60 months. The continuous wavelet transform revealed periodic fluctuation laws on multiple scales between nutrients and several environmental factors. Wavelet transform coherence (WTC) was performed on nutrients and environmental factors, and the results showed that temperature and dissolved oxygen (DO) have a strong influence on nutrient concentration fluctuation. The WTC revealed a weak correlation between pH and TP. On a longer period, however, pH was positively correlated with TN. The flow was found to be positively correct with N and P, while N and P were found to be negatively correct with DO and electrical conductance (EC) at different scales. In most cases, TP was negatively correlated with 5-day biochemical oxygen demand (BOD5) and permanganate index (CODMn). The correlation between TN and CODMn and BOD5 was limited, and no clear dominant phase emerged. In a nutshell, wavelet analysis revealed that water temperature, pH, DO, flow, EC, CODMn, and BOD5 had a pronounced influence on nutrient concentration in the River Yi at different time scales. In the case of the combination of environmental factors, pH and DO play the largest role in determining nutrient concentration.

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Section: Introductionmentioning

confidence: 99%