Closed House Chicken Barn Climate Control Using Fuzzy Inference System

The hazardous gases in chicken barn such as Ammonia (NH3) and hydrogen sulfide (H2S) are the health threats to the farm animals and workers which influenced by climate changes. The chicken barn requires real-time control to maintain the barn climate and monitor hazardous gases. The outdated on-off and proportional control are not so efficient in energy saving and productivity. The solution to monitor environment of the chicken barn is using wireless electronic nose (e-nose) and Short Messaging System (SMS). The e-nose system is used for the barn’s temperature and humidity data acquisition. The chicken barn climate control is utilizing fuzzy interface system. MATLAB software was used for the model which is developed based on Mamdani fuzzy interface system. The membership functions of fuzzy were generated, as well as the simulation and analysis of the climate control system. Results show that the performance of the fuzzy method can improve the system to control the barn’s climate. This system also provides real-time alerts to farmers based on specific limit value for the climate. It makes it easier for farmers to follow up on-site or remotely control the environmental conditions in the barn by using the SMS system

Using Model Predictive Control to Modulate the Humidity in a Broiler House and Effect on Energy Consumption

In moderate climate, broiler chicken houses are important heating energy consumers and hence heating fuel consumption accounts for a large part in operating costs. They can be reduced by constructional measures, which in turn lead to important costs as well. On the other hand, a software solution to reduce energy would lead to considerably less follow-up costs. The main objective of our work was to assess if it is possible to save energy with a software solution and eventually quantify the savings for a given broiler house in the Swiss Plateau. The investigation was carried out in simulation: the particular broiler house was measured, and a dynamical model for it was derived and validated. To actually search for a particular behaviour of the software that would lead to energy savings, model predictive control was used. The idea was not to specify a particular behaviour of the software but rather to let the software itself find the best behaviour in an exhaustive search. The simulations showed that energy savings can be realised mainly by letting the indoor humidity deviate from what usually is used as setpoint and hence take profit of the outdoor climate, which changes naturally during a 24-hour course. We used expert opinions to determine how long and large these setpoint deviations may be without harming the broilers. The simulations showed alsothat the light control and the biological activity of the animals reduced the potential savings

Temperature and Humidity Controlling System for Baby Incubator

Baby incubator is very important to keep the newborn’s body temperature especially for premature babies. The research aimed to design a baby incubator with controlled temperature and humidity. The incubator is designed to have a length of 60 cm, a width of 40 cm, and a height of 30 cm. System of baby incubator will automatically turn on or turn off the fan and or heating in accordance with the normal range of temperature and humidity in the incubator. The normal limits of temperature used is 33°C to 35°C. While the normal limits of air humidity in the incubator used is between 40% and 60%. Data acquisition system consists of temperature and humidity sensor, microcontroller ATmega8535, fan, heater, and LCD. LCD is used to display the results of measurements of temperature and humidity. Heater is used to regulate the temperature in the incubator. While fan is used to regulate the humidity in the incubator. Test results show that the heater will turn on if the temperature is below the limits of 33°C. While the fan will turn on if the humidity is above 60

Neural Predictive Control of Broiler Chicken Growth

Active control of the growth of broiler chickens has potential benefits for farmers in terms of improved production efficiency, as well as for animal welfare in terms of improved leg health. In this work, a differential recurrent neural network (DRNN) was identified from experimental data to represent broiler chicken growth using a recently developed nonlinear system identification algorithm. The DRNN model was then used as the internal model for nonlinear model predicative control (NMPC) to achieve a group of desired growth curves. The experimental results demonstrated that the DRNN model captured the underlying dynamics of the broiler growth process reasonably well. The DRNN based NMPC was able to specify feed intakes in real time so that the broiler weights accurately followed the desired growth curves ranging from $-12$ to +12% of the standard curve. The overall mean relative error between the desired and achieved broiler weight was 1.8% for the period from day 12 to day 51

Mathematical Model and Simulation Study of a Closed-poultry House Environment

The temperature and humidity inside poultry houses are highly coupled through nonlinear psychrometric processes, and the limitation of actuators makes this type of system difficult to control. To understand the dynamics of such systems and further to design a suitable controller, in this study, the mathematical model for a closed poultry house was derived from the governing equations of the various components related to the poultry house, including the energy and mass balance and the psychrometric correlations of the moist air. The model was simulated and the simulation result was compared to the data collected experimentally for model verification and control gains estimation. Under the assumptions of 70 percent Active Mixing Volume (AMV) with the constant maximum ventilation rate in the case study, the temperature and the relative humidity simulated results were in the good agreement with the real physical plant data.  At the front, the middle and the rear part of the poultry house, the root-mean-square error (RMSE) obtained for internal temperatures are 1.17oC, 0.68oC, and 0.46oC, respectively. And those data for relative humidity are 4.31%, 8.07%, and 53.54%, respectively