In this paper, a gray-box dynamic modeling approach for direct-expansion cooling systems is presented. The overall approach incorporates a multi-stage training procedure that consists of 1) identification of component sub-models from quasi-steady-state performance data, 2) system model integration with estimation of refrigerant charge and 3) fine tuning of thermal capacitances of the evaporator and condenser to capture the system dynamic responses. Compared to traditional physics-based models, the proposed modeling approach has advantages including reduced engineering efforts in the model development phase, improved computational efficiency and enhanced prediction accuracy. The modeling method was validated using a 3-ton variable-speed heat pump and proved to be capable of accurately predicting the system transient behaviors over a wide range of operating conditions. The established dynamic model was then applied for control stability analysis, with a specific goal of determining a proper control execution time step. The case study results showed that the stable control execution time step could change significantly, from 3 sec to 19 sec, as the operating conditions and control settings vary, and a proper selection of the execution time step is critical to ensure stable and reliable operations.

It is important to have practical methods for constructing and learning a good mathematical model for a building's thermal system in the presence of unmeasured disturbances and using data from closed loop operation. With this goal in mind, this paper presents a mathematical framework that explains the asymptotic behavior of an estimated model under those conditions and that can aid in learning an accurate model. Some analytic results from the literature of system identification are extended and interpreted for building systems. A new identification approach for determining an accurate thermal network (RC) model for a multi-zone building is developed based on the analytic result, and its superior performance over a conventional grey-box modeling approach is demonstrated experimentally.

Purpose

We evaluated the survival of patients with muscle invasive bladder cancer undergoing radical cystectomy without neoadjuvant chemotherapy to confirm the utility of existing clinical tools to identify low risk patients who could be treated with radical cystectomy alone and a high risk group most likely to benefit from neoadjuvant chemotherapy.

Materials and methods

We identified patients with muscle invasive bladder cancer who underwent radical cystectomy without neoadjuvant chemotherapy at our institution between 2000 and 2010. Patients were considered high risk based on the clinical presence of hydroureteronephrosis, cT3b-T4a disease, and/or histological evidence of lymphovascular invasion, micropapillary or neuroendocrine features on transurethral resection. We evaluated survival (disease specific, progression-free and overall) and rate of pathological up staging. An independent cohort of patients from another institution was used to confirm our findings.

Results

We identified 98 high risk and 199 low risk patients eligible for analysis. High risk patients exhibited decreased 5-year overall survival (47.0% vs 64.8%) and decreased disease specific (64.3% vs 83.5%) and progression-free (62.0% vs 84.1%) survival probabilities compared to low risk patients (p <0.001). Survival outcomes were confirmed in the validation subset. On final pathology 49.2% of low risk patients had disease up staged.

Conclusions

The 5-year disease specific survival of low risk patients was greater than 80%, supporting the distinction of high risk and low risk muscle invasive bladder cancer. The presence of high risk features identifies patients with a poor prognosis who are most likely to benefit from neoadjuvant chemotherapy, while many of those with low risk disease can undergo surgery up front with good expectations and avoid chemotherapy associated toxicity.