A bed includes components to control temperature of a sleep surface, for example based on time and historical usage patterns by a user. In some embodiments the temperature of the sleep surface is controlled based on information indicating a sleep state of the user. In some embodiments the temperature is dynamically adjusted so to achieve particular sleep states and/or sleep patterns for the user. In some embodiments the temperature and timing of temperature adjustments is iteratively adjusted over multiple sleep sessions so to achieve improvements in sleep states and/or sleep quality for the user.

Control of temperature of water delivered by a dual-stage water heater having an external boiler with a first heater as first stage and an internal duct (water booster) with a second heater as second stage. A control loop based on a measured water temperature in the boiler and controls the first heater. A second loop calculate a reference booster water temperature based on the temperature error at the outlet (difference between a measured outlet water temperature and a reference outlet water temperature) and controls second heater based on error between reference booster water temperature and measured booster water temperature. The reference outlet water temperature depends on type of beverage (e.g. espresso, cappuccino) and includes a temperature profile with different temperature for different sub-beverages (e.g. coffee, milk). Takes into account physical response times, inertia of heater and anticipate sudden changes of reference water temperature at hot water outlet.

Disclosed are systems, servers and methods for improving temperature profile control in a reactor with at least three fixed beds, exothermic reactions and interstage cooling. A model of the temperature differential across the first bed is developed and its error is used to infer unmeasured feed composition disturbances, which are used in the control of the downstream fixed beds for faster response to unmeasured feed composition changes and improved control of the temperature profile throughout the reactor. The first bed model error is then used as an input into an overall model that predicts reactor temperature profiles, which provides advanced notice of reactions in downstream beds, and enables efficient adjustment and compensation to a feed composition change. A Model Predictive Control (MPC) algorithm is applied to adjust the bed intercooling and first bed feed temperature so that the reactor temperature profile can be more precisely controlled.

A heating control method includes: obtaining a current inhalation parameter according to an inhalation situation of a user; obtaining a corresponding target temperature threshold according to the current inhalation parameter, the target temperature threshold being progressively increased in a stepwise manner as the inhalation parameter increases; and detecting a current heating temperature value of a heating body, and adjusting the current heating temperature value of the heating body to the target temperature threshold.

A masterless building device system for controlling an environmental condition of a building, includes a first building device and a second building device. The first building device includes a first processing circuit configured to determine, via a first sensor of the first building device, a first environmental condition value of the environmental condition. The first processing circuit is configured to broadcast the first environmental condition value to a second building device via a network. The first processing circuit is configured to receive, via the network, a second environmental condition value broadcast by the second building device, generate a calculated environmental condition value based on the first environmental condition value and the second environmental condition value, and control the environmental condition based on the calculated environmental condition value.

A thermostat for a building zone includes at least one of a model predictive controller and an equipment controller. The model predictive controller is configured to obtain a cost function that accounts for a cost of operating HVAC equipment during each of a plurality of time steps, use a predictive model to predict a temperature of the building zone during each of the plurality of time steps, and generate temperature setpoints for the building zone for each of the plurality of time steps by optimizing the cost function subject to a constraint on the predicted temperature. The equipment controller is configured to receive the temperature setpoints generated by the model predictive controller and drive the temperature of the building zone toward the temperature setpoints during each of the plurality of time steps by operating the HVAC equipment to provide heating or cooling to the building zone.

A thermostat my include a stored setpoint schedule, temperature sensors providing temperature sensor measurements; and a processing system configured to control an HVAC system based at least in part on the setpoint temperature and the temperature sensor measurements. The processing system may be configured to control the HVAC system by receiving an indication of a first time interval, where energy is available to the HVAC system at a first rate during the first time interval, energy is available to the HVAC system at a second rate during a second time interval that is outside of the first time interval, and the first rate is higher than the second rate; identifying a first one or more setpoints in the plurality of setpoints of the stored setpoint schedule that occur in the first time interval; and decreasing a temperature component of at least one of the first one or more setpoints.

An arrangement for monitoring an aseptic manufacturing process includes product condition sensors capable of making closely spaced measurements of a product condition such as temperature or humidity. The measurements are made using closely spaced sensors arranged in a linear array on a single probe, which may be used to take measurements at multiple levels within the product. Data from the sensors is transmitted to a data collection point via short range wireless digital communications. The sensors may be used to measure temperature and humidity at a single point. For example, when the sensors are used in pharmaceutical freeze drying, the location of a sublimation front may be calculated for each vial, and the freeze drying process may be controlled using the data.

Methods, systems and apparatuses are disclosed to control heating and cooling costs. A Household Utility Bill (or HUB) controller, including a processor, may receive a user input indicating a budget for temperature control costs. A user may provide the user input using a touchscreen display. The touchscreen display may be included on the HUB controller. Further, the HUB controller may determine, at predetermined intervals, an estimated cost associated with operating a heating, ventilation, and air conditioning (HVAC) unit. The HUB controller may compare the estimated cost with the budget. The HUB controller may transmit, using a wireless communication unit, an electronic message to a predetermined user device, including a request to confirm shutting off the HVAC unit based on a determination that the estimated cost equals or exceeds the budget. After the HVAC unit is shut off, the HUB controller may remain active but an electromechanical valve may become inactive.

Disclosed is a conditioner to condition nutritional substances with conditioning programs that power conditioning elements for percentage of the time of a repeating temporal cycle. During each temporal cycle, each required conditioning element may be activated for only a percentage of the full time of the temporal cycle. The conditioner includes temperature controls that are responsive to sensor feedback from the conditioner and that modify the conditioning element activation during the cycles.