©2012 This excerpt taken from the article of the same name which appeared in ASHRAE Journal, vol. 54, no. 11, November 2012.
By Derek Schrock, Member ASHRAE; Jimmy Sandusky, Associate Member ASHRAE; Andrey Livchak, Ph.D., Member ASHRAE
About the Authors
Derek Schrock is U.S. research director and Jimmy Sandusky is research engineer at Halton Company in Scottsville, Ky. Andrey Livchak, Ph.D. is director of global research and development at Halton Group Americas in Bowling Green, Ky.
Food service facilities have high energy consumption with equipment and commercial kitchen ventilation (CKV) being the primary energy consumers in a restaurant. Exhaust hood airflow drives HVAC energy consumption for CKV, so the first step in reducing this exhaust airflow is designing high efficiency hoods with low capture and containment (C&C) airflow rates. The next step is using demand control ventilation (DCV) to further reduce exhaust airflow when cooking is not taking place under the hood, but when appliances are hot and ready for food preparation.
Airflow reduction is not the sole objective of a DCV system; it also must ensure exhaust airflow and the corresponding supply airflows are increased to C&C levels as soon as cooking starts (to avoid spillage of convective heat and cooking effluent into the kitchen space). The current NFPA-96 Standard and International Mechanical Code require that a hood operate at full design airflows whenever full-load cooking activity occurs underneath an exhaust hood.
DCV has evolved from simple two-speed fan control systems to proportional control with variable frequency drives (VFDs) based on exhaust temperature. This improvement allowed for varying airflows throughout the day. Then, an optical sensor was added to the temperature-based control to detect cooking activity taking place under the exhaust hood to further enhance performance.
The latest system introduced to the market added measurement of exhaust airflow and automated balancing of multiple exhaust hoods connected to a single fan (or a dedicated fan) and modulation of replacement air for the space. Future systems need to be designed to consider the entire kitchen status to maximize energy savings.
Laboratory testing was conducted for common appliances in the commercial kitchen to evaluate system performance when equipped with various DCV algorithms: operating at a fixed exhaust setpoint temperature, operating on a temperature curve to increase exhaust airflow proportional to the temperature difference between exhaust and space temperature and operating on a temperature curve in combination with a cooking activity sensor (CAS) to drive the system to design when cooking is detected. Additionally, an evaluation was done to determine energy savings for a DCV system with balancing dampers installed on a four exhaust hood, island configuration.
The objective of the first round of tests was to compare performance of DCV systems that use temperature sensors only to those that incorporate cooking activity and temperature sensors. Currently, only two manufacturers offer the latter. One design uses optical opacity sensors to detect the presence of cooking effluent in a hood cavity. Another design uses infrared (IR) temperature sensors to monitor the surface temperature of cooking appliances. Data from these IR sensors along with space temperature and hood exhaust temperature sensors are analyzed to interpret the status of cooking appliances (idle, cooking or off) and adjust hood exhaust airflow accordingly.
A 72 in. (1.8 m) long wall canopy exhaust hood was configured to simulate various DCV control algorithms available on the market: exhaust temperature-based system that operates at a fixed setpoint, exhaust temperature-based system that operates on a curve and exhaust temperature coupled with a cooking activity sensor system (includes IR sensors).
Fixed setpoint exhaust temperatures of 90°F, 100°F and 130°F (32°C, 38°C and 54˚C) were evaluated. For these configurations, the minimum exhaust airflow rate was 80% of design airflow rate (a common value for temperature only based systems due to the limited ability to detect when cooking starts and ramp-up of exhaust airflow). Exhaust airflow was varied by a VFD in an attempt to maintain the tested temperature setpoint.
For exhaust temperature systems that operated on a curve, the minimum exhaust airflow rate was again 80% of design. When using the curve, exhaust airflow was incrementally increased as the temperature difference between exhaust and kitchen space increased. This algorithm ensures C&C of convective heat from appliances installed under the hood.
Minimum exhaust airflow for the system with cooking activity sensors installed was limited to 40% of the design rate to ensure that the exhaust fans were operated in their recommended range. This system used the “curve” temperature control as described previously when appliances are in idle mode and transitioned to design exhaust airflow for an adjustable period (which was set to seven minutes for this test) upon detection of cooking activity. Following the timer expiration, if no new cooking activity is detected, the system returns to the “curve” control algorithm.
The exhaust hood was installed at 80 in. (2 m) above finished floor with a temperature sensor mounted in the exhaust collar. The infrared sensors were positioned in the front, interior face of the canopy to sense the cooking surface. The temperature sensor was installed so that it was centered in the hood collar.
The test protocol included a range of appliances that all demonstrated similar trends, but due to space limitations only data for appliances most commonly seen in kitchens is presented: a charbroiler, griddle and open-vat fryer.
Airflows in Table 1 represent hood C&C airflow, and whenever the hood operates below this value when cooking occurs, the hood is spilling. Details of appliance fuel source and product cooked are shown as well.
During testing, each combination was evaluated at the idle and cooking states. Exhaust airflow rate and temperature were plotted versus time. The onset of the cooking process was noted to determine system response time.
Citation: ASHRAE Journal, vol. 54, no. 11, November 2012
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