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This research study proposes a classification system to identify and prevent occupational accidents in fiber storage warehouses at a pulp and paper facility. The present analysis is based on variables including pedestrian circulation, bobcat, trailer trucks, access, pedestrian circulation zones, and handrails. The proposed methodology defines and trains the system by using occupational accident event data collected at the facility. Three different predicting algorithms are used: J48 decision-making trees, Naive Bayes, and Bayesian nets. The results show that the J48 decision-making tree algorithm accurately identifies possible occupational accidents 90% of the time. It is concluded that identifying variables involved in occupational accidents allows generating a C4.5 (J48) decision-making tree that can be used as a support tool to prevent occupational accidents. [ABSTRACT FROM AUTHOR]

A newly commissioned pulp moulding plant presents potential technical risks associated with the technology transfer process, which could limit the economic benefits and process efficiency indicators. The objective of the present work is to assess the production technology of moulded fibre by implementing a technology assessment model. The model includes the application of water and fiber, heat and mechanical energy balances; design and operation evaluation of equipment, such as oven, cooling tower, dissolved air flotation system and piping system, using DesDaf and dP software. The process is evaluated with TIBCO Business Studio simulator, which facilitates examination of technical and economic criteria that contribute to decision making. The implementation of the model made possible to verify the magnitude of the design and operating variables defined in plant documents, the identification of the potential deficiencies of the technology and the proposal of correction of a deficiency in the technology with the corresponding economic evaluation. [ABSTRACT FROM AUTHOR]