The viscometer is a system measuring the viscosity value of a liquid substance. The function of this viscosity measuring instrument is used to analyze the viscosity level of a particular product so that it is easy to know the quality of fluid viscosity. In this research, two methods were conducted, namely simulation and experimentation. Simulations were conducted using Simulink Matlab while experiments were conducted by designing fluid viscosity measuring instruments by rotary method or commonly known as a rotational viscometer. This tool uses two sensors namely, rpm sensor to measure rpm of dc motor and current and voltage sensor to measure current from dc motor, and also use controller equipped with Internet of Things (IoT) so that the measurement results will be displayed through LCD and can be monitored through the website. From the simulation results obtained drum motor spinning at low speed to the 10th second of the motor rotation about 6 rad / s and as time increases, the motor rotation will increase until the 60th second of the motor rotation begins to be constant this is due to the large shear voltage produced by a fluid at the beginning of the motor is turned on very large and decreases over time. While the test results of the tool that has been designed, obtained the measurement results on SAE 40 Oil obtained accuracy results of 0.99, on the measurement of SAE Oil 20W-50 obtained an accuracy value of 0.99 and in the measurement of SAE 10W-30 Oil obtained an accuracy value of 0.99.
Expansion of land for the development of human civilization reduces natural ecosystems. Especially land for agriculture and fisheries in densely populated areas. The more the population, the more food needs. One way to deal with this problem is aquaponic cultivation. Aquaponics is a modern practical farming system that integrates plant cultivation systems with aquatic animal cultivation. In aquaponics cultivation there are factors that need to be considered. One of these success factors is the Temperature and Automatic Feeder in the pond. To get the temperature needed by the fish, temperature control is carried out using the DS18B20 sensor. When the fish pond temperature drops below 28°C, the heater will automatically turn on to raise the pond temperature, and when the pond temperature rises above 32°C, the peltier will turn on and start lowering the pond temperature. Feeding is one of the success factors for aquaponics. If the feed given does not match the weight and age of the fish, the growth of the fish can be disrupted, causing the fish to die. Automatic feeding uses the RTC module as the set time and the servo motor as the opening valve for the fish feed container. Fish were fed at 08.00, 12.00, and 17.00. The average error obtained after validating the DS18B20 sensor is 0.61. The accuracy obtained is 98.05%. The ratio of fish RGR before and after the control system was installed was 7.14% and 11.2 The fish FCR values before and after the control system was installed were 12.50% and 21.07%. The plant growth rates before and after the control system was installed were 22.9% and 33.71%.
Biogas impurities CO2 and H2S decrease the quality of biogas, which leads to a reduced caloric value and corrosive behavior, respectively. A vertical/columnar wet scrubber has been widely employed for biogas purification in which the absorption of impurities strongly depends on the contact time and surface area between the biogas and water. The drawback of this method lies in the stability of CH4 production due to the influence of the bioreactor conditions and the fluctuating condition of the surrounding environment. In this work, we present a novel design of simple water absorption columns embedded with an ultrasonic nebulizer for biogas purification. In this designed system, CO2 and H2S become dissolved in the water, as the CH4 characterized by water low solubility is released on the surface of the water by using an ultrasonic diffuser/nebulizer. We optimized the water absorption performance by varying the water pH in the range 6.0-7.3. The results indicate that water pH affects biogas purification in the designed system. The optimum pH condition was 6.78, which yielded CH4 enrichment of 11%, O2 increase of 29%, CO2 reduction of 32%, and H2S reduction of 99.8%. To evaluate the biogas purification process in the upscaled system, a model and SIMULINK-based simulation were developed to predict the biogas purification process.
The research consists of two parts, the first one is to design the dynamic plant model of polishing unit using artificial neural network (ANN) type backpropagation, and the second one is to design a simulation of a close loop control system on Simulink consisting of logic solver, control valve and ANN polishing unit. The ANN polishing unit was trained and obtained the best model structure 4-24-3 with four inputs chemical oxygen demand (COD) influent, oil in water (OIW) influent, urea, and triple superphosphate (TSP), twenty-four hidden layer nodes, and three outputs (OIW effluent, COD effluent and dissolved oxygen (DO)). The mean square error (MSE) and root mean square error (RMSE) from ANN trained were 0.00485 and 0.06964, obtained by the second iteration. From the simulation results on Simulink by giving several scenarios in the logic solver condition table, the action is brought in the form of urea and TSP nutrition issued by the control valve. The values are used to achieve the DO setpoint (2 mg/L), among others: when COD and OIW influent exceed the quality standard, COD exceeds the quality standard, and OIW does not exceed the quality standard, and the DO error is below zero.
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