TY - JOUR
T1 - Real-time monitoring of the moisture content of filter cakes in vacuum filters by a novel soft sensor
AU - Huttunen, Manu
AU - Nygren, Lauri
AU - Kinnarinen, Teemu
AU - Ekberg, Bjarne
AU - Lindh, Tuomo
AU - Karvonen, Vesa
AU - Ahola, Jero
AU - Häkkinen, Antti
N1 - Funding Information:
The authors would like to thank Hanna Niemelä for providing language help during the writing process. Funding: This work was supported by The Finnish Funding Agency for Technology and Innovation (Tekes).
Funding Information:
Funding: This work was supported by The Finnish Funding Agency for Technology and Innovation ( Tekes ).
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/9/15
Y1 - 2019/9/15
N2 - The moisture content of filter cakes is probably the most important characteristic that should be kept at a desired level in industrial cake filtration applications to maintain consistent product quality and minimize energy consumption. Most of the currently applied methods for contactless real-time monitoring of the moisture content are based for example on x-ray or microwave techniques, and therefore, the equipment for the purpose is highly specialized. This paper introduces a novel soft sensor for filter cake moisture estimation that uses machine learning algorithms and data collected with basic process instrumentation. The method is primarily based on the cooling effect observed in the cake and air, caused by evaporation of liquid from the cake during the dewatering period, and it can be supported by other process data. The specific energy consumption of vacuum filtration and the subsequent thermal drying to zero moisture is also analyzed. The results of pilot-scale experiments with calcite slurry and a horizontal belt vacuum filter show that in order to minimize the specific energy consumption of vacuum filtration, it is crucial to find the right combination of slurry concentration, vacuum level, and mass of filter cake per unit area. The proposed method for estimating the filter cake moisture content is especially suitable for real-time monitoring and control, enabling also considerable reduction in the energy consumption of the overall process. When applying the proposed soft sensor method in a pilot-scale process, the mean absolute error of the estimated moisture content of the filter cake is ∼0.4 percentage points when the temperature of air at the vacuum pump inlet and the vacuum pump air flow rate are included in the input variables.
AB - The moisture content of filter cakes is probably the most important characteristic that should be kept at a desired level in industrial cake filtration applications to maintain consistent product quality and minimize energy consumption. Most of the currently applied methods for contactless real-time monitoring of the moisture content are based for example on x-ray or microwave techniques, and therefore, the equipment for the purpose is highly specialized. This paper introduces a novel soft sensor for filter cake moisture estimation that uses machine learning algorithms and data collected with basic process instrumentation. The method is primarily based on the cooling effect observed in the cake and air, caused by evaporation of liquid from the cake during the dewatering period, and it can be supported by other process data. The specific energy consumption of vacuum filtration and the subsequent thermal drying to zero moisture is also analyzed. The results of pilot-scale experiments with calcite slurry and a horizontal belt vacuum filter show that in order to minimize the specific energy consumption of vacuum filtration, it is crucial to find the right combination of slurry concentration, vacuum level, and mass of filter cake per unit area. The proposed method for estimating the filter cake moisture content is especially suitable for real-time monitoring and control, enabling also considerable reduction in the energy consumption of the overall process. When applying the proposed soft sensor method in a pilot-scale process, the mean absolute error of the estimated moisture content of the filter cake is ∼0.4 percentage points when the temperature of air at the vacuum pump inlet and the vacuum pump air flow rate are included in the input variables.
KW - Dewatering
KW - Moisture content prediction
KW - Soft sensor
KW - Thermodynamics
KW - Vacuum filtration
UR - http://www.scopus.com/inward/record.url?scp=85065559202&partnerID=8YFLogxK
U2 - 10.1016/j.seppur.2019.03.091
DO - 10.1016/j.seppur.2019.03.091
M3 - Article
AN - SCOPUS:85065559202
SN - 1383-5866
VL - 223
SP - 282
EP - 291
JO - Separation and Purification Technology
JF - Separation and Purification Technology
ER -