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Journal articles
Open Access
Ethylene-carbon monoxide extrudable adhesive copolymers for polyvinylidene chloride, June 1988, TAPPI Journal 88JUN140

Ethylene-carbon monoxide extrudable adhesive copolymers for polyvinylidene chloride, TAPPI JOURNAL June 1988

Journal articles
Open Access
Development of waterborne laminating adhesive systems, June 1988, TAPPI Journal 88JUN145

Development of waterborne laminating adhesive systems, TAPPI JOURNAL June 1988

Journal articles
Open Access
Formulating to enhance the radiation cross linking of thermoplastic rubber for the hot-melt pressure-senstivie adhesives, June 1988, TAPPI Journal 88JUN155

Formulating to enhance the radiation crosslinking of thermoplastic rubber for hot-melt pressure-sensitive adhesives, TAPPI JOURNAL June 1988

Journal articles
Open Access
Fiber flocculation in pulp suspension flow-Part 2: Experimental results, TAPPI JOURNAL June 1988

Fiber flocculation in pulp suspension flow-Part 2: Experimental results, TAPPI JOURNAL June 1988

Journal articles
Open Access
Improved control of drum level for boilers with "shrink" and "swell" problems, June 1988, TAPPI Journal 88JUN65

Improved control of drum level for boilers with "shrink" and "swell" problems, TAPPI JOURNAL June 1988

Journal articles
Open Access
The resistance of refiner plate alloys to bar rounding, July 1988, TAPPI Journal 88JUN95

The resistance of refiner plate alloys to bar rounding, TAPPI JOURNAL June 1988

Journal articles
Open Access
Upgrading wood chips: the Paprifer process, TAPPI JOURNAL March 1988

Upgrading wood chips: the Paprifer process, TAPPI JOURNAL March 1988

Journal articles
Open Access
Optimization of a swirl burner for pulverized-wood fuels, TAPPI JOURNAL May 1988 88MAY91

Optimization of a swirl burner for pulverized-wood fuels, TAPPI JOURNAL May 1988

Journal articles
Open Access
On-line layer discrimination of multilayer materials, TAPPI JOURNAL May 1988 88MAY97

On-line layer discrimination of multilayer materials, TAPPI JOURNAL May 1988

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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