Model implementation for dynamic computation of system cost for advanced life support.

Life support system designs for long-duration space missions have a multitude of requirements drivers, such as mission objectives, political considerations, cost, crew wellness, inherent mission attributes, as well as many other influences. Evaluation of requirements satisfaction can be difficult, particularly at an early stage of mission design. Because launch cost is a critical factor and relatively easy to quantify, it is a point of focus in early mission design. The method used to determine launch cost influences the accuracy of the estimate. This paper discusses the appropriateness of dynamic mission simulation in estimating the launch cost of a life support system. This paper also provides an abbreviated example of a dynamic simulation life support model and possible ways in which such a model might be utilized for design improvement.

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.

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.

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.

Calculation of mean cell residence time for unsteady-state activated sludge systems.

A formulation to calculate the mean cell residence time (MCRT or sludge age) of unsteady-state activated sludge systems is presented. The formulation was studied by applying it to data generated by computer simulation and to data obtained from an actual wastewater treatment plant. The computer simulation study allowed the effects of step and pulse changes in biochemical oxygen demand (BOD) loading, and step changes in a control variable, waste sludge flow rate, to be studied independently of each other and of other disturbances. The unsteady-state MCRT formulation (herein called the dynamic sludge age, or DSA) was found to be an improvement over the traditional steady-state calculation, both for process control, and for research into activated sludge dynamics.