Add to ORKGMachine learning techniques are compared to predict nitrogen oxide (NOx) pollutant emission from the recovery boiler of a Kraft pulp mill. Starting from a large database of raw process data related to a Kraft recovery boiler, we consider a regression problem in which we are trying to predict the value of a continuous variable. Generalization is done on the worst case configuration possible to make sure the model is adequate: the training period concerns stationary operations while test periods mainly focus on NOx emissions during transient operations
Predictive emission monitoring systems (PEMS) are software solutions for the validation and suppleme...
In this paper, we tackle air quality forecasting by using machine learning approaches to predict the...
Predictive emission monitoring systems (PEMS) are important tools for validation and backing up of c...
In this paper, machine learning techniques are compared to predict nitrogen oxide (NOx) pollutant e...
In this paper, supervised learning techniques are compared to predict nitro- gen oxide (NOx) pollut...
The broad objective of this thesis is to apply and compare supervised learning techniques for predic...
Machine learning (ML) plays an important role in atmospheric environment prediction, having been wid...
Nitrous oxide (N2O) emissions may account for up to 80 % of a wastewater treatment plant's (WWTP) to...
Abstract. Nitric acid production plants emit small amounts of nitrogen oxides (NOx) to the environme...
Predicting the state of modern heavy-duty gas turbines for large-scale power generation allows for m...
Current studies show that traditional deterministic models tend to struggle to capture the non-linea...
Current studies show that traditional deterministic models tend to struggle to capture the non-linea...
This paper is aiming to apply neural network algorithm for predicting the process response (NOx emis...
In order to establish countermeasures for air pollution, it is first necessary to accurately grasp t...
In this paper, a nonparametric kernel prediction algorithm in machine learning is applied to predict...
Predictive emission monitoring systems (PEMS) are software solutions for the validation and suppleme...
In this paper, we tackle air quality forecasting by using machine learning approaches to predict the...
Predictive emission monitoring systems (PEMS) are important tools for validation and backing up of c...
In this paper, machine learning techniques are compared to predict nitrogen oxide (NOx) pollutant e...
In this paper, supervised learning techniques are compared to predict nitro- gen oxide (NOx) pollut...
The broad objective of this thesis is to apply and compare supervised learning techniques for predic...
Machine learning (ML) plays an important role in atmospheric environment prediction, having been wid...
Nitrous oxide (N2O) emissions may account for up to 80 % of a wastewater treatment plant's (WWTP) to...
Abstract. Nitric acid production plants emit small amounts of nitrogen oxides (NOx) to the environme...
Predicting the state of modern heavy-duty gas turbines for large-scale power generation allows for m...
Current studies show that traditional deterministic models tend to struggle to capture the non-linea...
Current studies show that traditional deterministic models tend to struggle to capture the non-linea...
This paper is aiming to apply neural network algorithm for predicting the process response (NOx emis...
In order to establish countermeasures for air pollution, it is first necessary to accurately grasp t...
In this paper, a nonparametric kernel prediction algorithm in machine learning is applied to predict...
Predictive emission monitoring systems (PEMS) are software solutions for the validation and suppleme...
In this paper, we tackle air quality forecasting by using machine learning approaches to predict the...
Predictive emission monitoring systems (PEMS) are important tools for validation and backing up of c...