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Journal articles
Open Access
Infrared analysis for process control in the pulp and paper industry, TAPPI JOURNAL, October 2000, Vol. 83(10)

Infrared analysis for process control in the pulp and paper industry, TAPPI JOURNAL, October 2000, Vol. 83(10)

Magazine articles
Open Access
Challenges and opportunities for china's paper industry, TAPPI JOURNAL, October 2000, Vol. 83(10)

Challenges and opportunities for china's paper industry, TAPPI JOURNAL, October 2000, Vol. 83(10)

Magazine articles
Open Access
People development in research and technology organizations, TAPPI JOURNAL, October 2000, Vol. 83(10)

People development in research and technology organizations, TAPPI JOURNAL, October 2000, Vol. 83(10)

Journal articles
Open Access
Impulse drying of board grades: converting trials, TAPPI JOURNAL, October 2000, Vol. 83(10)

Impulse drying of board grades: converting trials, TAPPI JOURNAL, October 2000, Vol. 83(10)

Magazine articles
Open Access
Taiwan holds first symposium on environmentally friendly and emerging technologies for a sustainable industry, TAPPI JOURNAL, September 2000, Vol. 83(9)

Taiwan holds first symposium on environmentally friendly and emerging technologies for a sustainable industry, TAPPI JOURNAL, September 2000, Vol. 83(9)

Journal articles
Open Access
Steambox comparator experiments: apparatus validation and investigation of steambox performance, TAPPI JOURNAL, September 2000, Vol. 83(9)

Steambox comparator experiments: apparatus validation and investigation of steambox performance, TAPPI JOURNAL, September 2000, Vol. 83(9)

Journal articles
Open Access
Water minimization in the washing section of a paperboard mill, TAPPI JOURNAL, September 2000, Vol. 83(9)

Water minimization in the washing section of a paperboard mill, TAPPI JOURNAL, September 2000, Vol. 83(9)

Magazine articles
Open Access
What's new with tappi test methods?, TAPPI JOURNAL, September 2000, Vol. 83(9)

What's new with tappi test methods?, TAPPI JOURNAL, September 2000, Vol. 83(9)

Journal articles
Open Access
Use of enzymes for reduction in refining energy - laboratory

Use of enzymes for reduction in refining energy - laboratory studies, TAPPI JOURNAL, November 2006

Journal articles
Open Access
Data driven modeling to reduce fossil fuel consumption in a lime kiln integrated with biomass gasifier, TAPPI Journal September 2026

ABSTRACT: Biomass gasification, although already known and applied, is currently being consolidated as a sustainable alternative in the pulp industry, contributing to the reduction of fossil carbon dioxide (CO2) emissions and to the utilization of forest residues. In this context, pulp mills are beginning to adopt biomass gasification within their chemical recovery cycles by integrating the technology with lime kilns. However, further process studies are still needed to support and optimize this application. Thus, the present study aimed to analyze lime mud feed temperature by applying an artificial neural network model to a gasification system integrated with a lime kiln. For this evaluation, 10 periods of system stability throughout 2024 were selected under different operational conditions. Data from 28 potential predictive variables were collected, a total of 348 observations. This dataset was then processed using R software, where data treatment and model dimensionality reduction were performed, resulting in 10 predictive variables between gasifier and kiln. Next, the dataset was randomly divided into training (70% of the observations) and testing data. The established neural network model (using “neuralnet” package) was optimized, resulting in a configuration containing one hidden layer with three neurons. This setup enabled optimal estimation of the flue gas kiln outlet temperature, with mean absolute error (MAE) = 3.0°C and root mean square error (RMSE) = 4.1°C, applying the resilient backpropagation algorithm with backtracking — both below the thermocouple’s measurement error (±5.0°C) for the evaluated average temperature range (668.2°C). When the same dataset was modeled using the resilient backpropagation algorithm without backtracking, even better results were achieved: MAE = 2.5°C and RMSE = 3.7°C. Thus, after evaluating these and other configurations, it was concluded that the best model required 10 predictive variables and the backpropagation algorithm without backtracking to determine flue gas kiln outlet temperature. These results provide a better understanding of how gasifier and kiln variables influence the temperature in the lime kiln, which is essential to improve control and optimize the calcination process, avoiding supplementary fossil fuel consumption.

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