A Real-Time Simulation Tool for Fault Detection and Diagnosis of HVAC Systems

In this study, a real-time simulation tool was developed for online monitoring, control and diagnosis of HVAC systems. A twozone variable air volume terminal reheat (VAV-TRH) HVAC system is considered. The developed program can be used in offline and online environments. The offline environment allows the operators to examine optimal control strategies, and to investigate problems associated with improper size of components which could be the root cause of the fault. The online environment is useful for monitoring, control and diagnosis of HVAC systems. A set of expert rules were applied to identify the faults. Simulation results show that the developed tool is able to correctly identify the fault patterns and therefore can be used for improving operating performance of HVAC systems. 

Zaheeruddin M. Neuro-optimal operation of a variable air volume HVAC&R system

a b s t r a c t Low operational efficiency especially under partial load conditions and poor control are some reasons for high energy consumption of heating, ventilation, air conditioning and refrigeration (HVAC&R) systems. To improve energy efficiency, HVAC&R systems should be efficiently operated to maintain a desired indoor environment under dynamic ambient and indoor conditions. This study proposes a neural network based optimal supervisory operation strategy to find the optimal set points for chilled water supply temperature, discharge air temperature and VAV system fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. Simulation results show that compared to the conventional night reset operation scheme, the optimal operation scheme saves around 10% energy under full load condition and 19% energy under partial load conditions

Self-Tuning Dynamic Models of HVAC System Components

A great majority of modern buildings are equipped with Energy Management and Control Systems (EMCS) which monitor and collect operating data from different components of heating ventilating and air conditioning (HVAC) systems. Models derived and tuned by using the collected data can be incorporated into the EMCS for online prediction of the system performance. To that end, HVAC component models with self-tuning parameters were developed and validated in this paper. The model parameters were tuned online by using a genetic algorithm which minimizes the error between measured and estimated performance data. The developed models included: a zone temperature model, return air enthalpy/humidity and CO2 concentration models, a cooling and heating coil model, and a fan model. The study also includes tools for estimating the thermal and ventilation loads. The models were validated against real data gathered from an existing HVAC system. The validation results show that the component models augmented with an online parameter tuner, significantly improved the accuracy of predicted outputs. The use of such models offers several advantages such as designing better real-time control, optimization of overall system performance, and online fault detection

EXTRACTION STUDIES ON MALONIC ACID

Extraction studies on malonic acid from its aqueous solutions have been made by various solvents at 25 ±1°C. Both aromatic and aliphatic solvents have been investigated. A comparison with reference to the extraction efficiency has been made in terms of the dipole moments of the solvents. In general aliphatic solvents have been found more suitable than the aromatic solvents

Insights into isotherms, kinetics, and thermodynamics of adsorption of acid blue 113 from an aqueous solution of nutraceutical industrial fennel seed spent

Abstract Research studies have been carried out to accentuate Fennel Seed Spent, a by-product of the Nutraceutical Industry, as an inexpensive, recyclable and operational biosorbent for bioremediation of Acid Blue 113 (AB113) in simulated water-dye samples and textile industrial effluent (TIE). The physical process of adhesion of AB113 on the surface of the biosorbent depends on various parameters, such as the initial amount of the dye, amount and expanse of the biosorbent particles, pH of the solution and temperature of the medium. The data obtained was analyzed using three two-parameter and five three-parameter adsorption isotherm models to glean the adsorbent affinities and interaction mechanism of the adsorbate molecules and adsorbent surface. The adsorption feature study is conducted employing models of Weber-Morris, pseudo 1st and 2nd order, diffusion film model, Dumwald-Wagner and Avrami model. The study through 2nd order pseudo and Avrami models produced complementary results for the authentication of experimental data. The thermodynamic features, ΔG 0 , ΔH 0, and ΔS 0 of the adsorption process are acclaimed to be almost spontaneous, physical in nature and endothermic in their manifestation. Surface characterization was carried out using Scanner Electron Microscopy, and identification and determination of chemical species and molecular structure was performed using Infrared Spectroscopy (IR). Maximum adsorption evaluated using statistical optimization with different combinations of five independent variables to study the individual as well as combined effects by Fractional Factorial Experimental Design (FFED) was 236.18 mg g−1 under optimized conditions; pH of 2, adsorbent dosage of 0.500 g L−1, and an initial dye concentration of 209.47 mg L−1 for an adsorption time of 126.62 min with orbital shaking of 165 rpm at temperature 49.95 °C