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Journal articles
Open Access
Paper machine maintenance in a brazilian plant, TAPPI JOURNAL, June 1998, Vol. 81(6)

Paper machine maintenance in a brazilian plant, TAPPI JOURNAL, June 1998, Vol. 81(6)

Journal articles
Open Access
Engineers and papermakers bond for better papermaking at TAPPI's first SuperConference, TAPPI JOURNAL, March 1998, Vol. 81(3)

Engineers and papermakers bond for better papermaking at the 1997 superconference, TAPPI JOURNAL, March 1998, Vol. 81(3)

Journal articles
Open Access
Factors controlling the passage of fibers through slots, TAPPI JOURNAL, May 1998, Vol. 81(5)

Factors controlling the passage of fibers through slots, TAPPI JOURNAL, May 1998, Vol. 81(5)

Journal articles
Open Access
New technology for papermaking: Commercializing biopulping, TAPPI JOURNAL, November 1998, Vol. 81(11)

New technology for papermaking: commercializing biopulping, TAPPI JOURNAL, November 1998, Vol. 81(11)

Journal articles
Open Access
Paper science and engineering programs - a comparison of the curricula, TAPPI JOURNAL, October 1998, Vol.81(10)

Paper science and engineering programs - a comparison of the curricula, TAPPI JOURNAL, October 1998, Vol.81(10)

Journal articles
Open Access
A business-oriented approach to maintenance, TAPPI JOURNAL, September 1998, Vol. 81(9)

On management: a business-oriented approach to maintenance, TAPPI JOURNAL, September 1998, Vol. 81(9)

Journal articles
Open Access
Shop talk: Implementing an effective boilout program, TAPPI JOURNAL, September 1998, Vol. 81(9)

Shop talk: implementing an effective boilout program, TAPPI JOURNAL, September 1998, Vol. 81(9)

Journal articles
Open Access
Energy efficient motor and drive systems: Your path to profit, TAPPI JOURNAL, September 1998, Vol. 81(9)

Energy efficient motor and drive systems: your path to profit, TAPPI JOURNAL, September 1998, Vol. 81(9)

Journal articles
Open Access
The Effects of cyclic axial loading of single wood pulp fibers at elevated temperature and humidity, TAPPI JOURNAL, April 1999, Vol. 82(4)

The Effects of cyclic axial loading of single wood pulp fibers at elevated temperature and humidity, TAPPI JOURNAL, April 1999, Vol. 82(4)

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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