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A neural fuzzy system was used to investigate the influence of environmental variables (time, aeration, moisture, and particle size) on composting parameters (pH, organic matter [OM], nitrogen [N], ammonium nitrogen [NH 4 ϩ -N] and nitrate nitrogen [NO 3 Ϫ -N]). This was to determine the best composting conditions to ensure the maximum quality on the composts obtained with the minimum ammonium losses. A central composite experimental design was used to obtain the neural fuzzy model for each dependent variable. These models, consisting of the four independent process variables, were found to accurately describe the composting process (the differences between the experimental values and those estimated by using the equations never exceeded 5-10% of the former). Results of the modeling showed that creating a product with acceptable chemical properties (pH, NH 4 ϩ -N and NO 3 Ϫ -N) entails operating at medium moisture content (55%) and medium to high particle size (3-5 cm). Moderate to low aeration (0.2 L air/min ⅐ kg) would be the best compromise to compost this residue because of the scant statistical influence of this independent variable.

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