Real-time monitoring of the moisture content of filter cakes in vacuum filters by a novel soft sensor

Manu Huttunen, Lauri Nygren, Teemu Kinnarinen, Bjarne Ekberg, Tuomo Lindh, Vesa Karvonen, Jero Ahola, Antti Häkkinen

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)282-291
Number of pages10
JournalSeparation and Purification Technology
Volume223
DOIs
Publication statusPublished - 15 Sep 2019
MoE publication typeA1 Journal article-refereed

Fingerprint

Moisture
Vacuum
Monitoring
Sensors
Energy utilization
Vacuum pumps
Air
Calcium Carbonate
Calcite
Dewatering
Learning algorithms
Learning systems
Drying
Evaporation
Microwaves
Flow rate
Cooling
X rays
Liquids
Experiments

Keywords

  • Dewatering
  • Moisture content prediction
  • Soft sensor
  • Thermodynamics
  • Vacuum filtration

Cite this

Huttunen, Manu ; Nygren, Lauri ; Kinnarinen, Teemu ; Ekberg, Bjarne ; Lindh, Tuomo ; Karvonen, Vesa ; Ahola, Jero ; Häkkinen, Antti. / Real-time monitoring of the moisture content of filter cakes in vacuum filters by a novel soft sensor. In: Separation and Purification Technology. 2019 ; Vol. 223. pp. 282-291.
@article{51c7765d6e65454ca855c6562ab377d3,
title = "Real-time monitoring of the moisture content of filter cakes in vacuum filters by a novel soft sensor",
abstract = "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.",
keywords = "Dewatering, Moisture content prediction, Soft sensor, Thermodynamics, Vacuum filtration",
author = "Manu Huttunen and Lauri Nygren and Teemu Kinnarinen and Bjarne Ekberg and Tuomo Lindh and Vesa Karvonen and Jero Ahola and Antti H{\"a}kkinen",
year = "2019",
month = "9",
day = "15",
doi = "10.1016/j.seppur.2019.03.091",
language = "English",
volume = "223",
pages = "282--291",
journal = "Separation and Purification Technology",
issn = "1383-5866",
publisher = "Elsevier",

}

Real-time monitoring of the moisture content of filter cakes in vacuum filters by a novel soft sensor. / Huttunen, Manu; Nygren, Lauri; Kinnarinen, Teemu; Ekberg, Bjarne; Lindh, Tuomo; Karvonen, Vesa; Ahola, Jero; Häkkinen, Antti.

In: Separation and Purification Technology, Vol. 223, 15.09.2019, p. 282-291.

Research output: Contribution to journalArticleScientificpeer-review

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

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

VL - 223

SP - 282

EP - 291

JO - Separation and Purification Technology

JF - Separation and Purification Technology

SN - 1383-5866

ER -