Biomedical subjects

D A Vaccari

Publications and source records attributed to D A Vaccari.

3 recordsLinked to original sources

PubMed1999

Multivariable empirical modeling of ALS systems using polynomials.

Multivariable polynomial regression (MPR) was used to model plant motion time-series and nutrient recovery data for Advanced Life Support (ALS). MPR has capabilities similar to neural network models in terms of ability to fit multiple-input single-output nonlinear data. It has advantages over neural networks including: reduced overfitting, produces models that are more tractable for optimization, sensitivity analysis, and prediction of confidence intervals. MPR was used to produce nonlinear polynomial time-series models predicting plant projected canopy area versus time and temperature. Temperature was found to not have a statistically significant effect. Models were developed to relate rate and extent of nutrient recovery to treatment parameters, including temperature and use of heat pretreatment or nutrient supplementation. These applications demonstrate MPR's capability to fill "gaps" in an integrated model of ALS. Fundamental models should be used whenever available. However, some components may require empirical modeling. Furthermore, even fundamental models often have empirical constituents. MPR models are proposed to satisfy these needs.

Biodegradation, Environmental↗

PubMed1998

Nutrient recovery and biodegradation of inedible tomato plant residues by activated sludge cultures and Phanerochaete chrysosporium.

The biodegradation of inedible biomass and the recovery of nutrients from hydroponically grown tomato plant material were investigated under various growth conditions of activated sludge and the fungus Phanerochaete chrysosporium. The experiments were carried out in shaker flasks at three incubation temperatures (25 degrees C, 40 degrees C, and 60 degrees C for the activated sludge and 25 degrees C, 40 degrees C, and 50 degrees C for the fungi) with heat-pretreated samples at 150 degrees C for 30 min, and without pretreatment of the inedible residues. Under the experimental conditions tested, both cultures exhibited similar performance in terms of solids reduction and nutrient recovery. Solids reduction as high as 70% was obtained in both systems. Most of the solids degradation occurred the first 16 days of incubation. Cellulose degradation reached about 90% but no significant reduction in the solids lignin content was observed. Recovery of nitrogen (as NO2-N and NO3-N) and other micronutrients was sufficiently high and was accompanied by an average 70% reduction in COD, indicating that the final effluent is suitable for hydroponic plant growth. Incubation temperature had a minimal effect on solids degradation but appeared to influence the leachability of certain nutrients.

Bacteria↗

PubMed1992

Generalized multiple-regression techniques with interaction and nonlinearity for system identification in biological treatment processes.

A class of multiple regression models, called "generalized multiple-regression" (GMR) is proposed. GMR has the advantages of being easy and rapid to fit, and uses standard multilinear regression software. It has an advantage over ARIMA models in modeling nonlinearity and linear and nonlinear interactions among variables. Its main disadvantage is that, if there are many independent variables, the reduction of degrees of freedom may be important. It is less parsimonious than other models, but availability of increased computational power makes this not a serious disadvantage. The GMR models are compared to autoregressive transfer function models and feedforward back propagation neural network models. In the case of modeling effluent volatile suspended solids, GMR models were superior to both linear autoregressive models and neural network models. The neural network models did, however, outperform the linear models. In the case of modeling sludge volume index, both GMR and the neural network model were unable to improve upon ARIMA models. It was concluded that ARIMA models may, in some cases, produce the most parsimonious model, but in other cases they may miss important process behaviors. The GMR models showed robust capability to describe complex data.

Models, Theoretical↗