Raspberry Pi

Overview of Precision Pressure Brewing Traditional coffee brewing often relies on manual intuition, but the MokaBot represents a shift toward algorithmic precision. The core challenge of the Moka%20Pot lies in its volatile temperature fluctuations; as water leaves the bottom chamber, the air expands rapidly, often overheating the grounds and creating bitter flavors. By integrating a PID%20Controller, the MokaBot prototype automates the 'gas management' phase of brewing, ensuring a steady, low-temperature flow that highlights the nuanced notes of specialty coffee. Prerequisites and Logic Fundamentals Before implementing a control loop like this, you need a basic understanding of sensor feedback systems. The logic relies on a closed-loop system: the hardware reads a temperature, compares it to a target (the set point), and adjusts the power output accordingly. Familiarity with C++ for Arduino or Python for Raspberry%20Pi helps when translating these mathematical concepts into executable code. Key Libraries & Tools - **PID Library (e.g., Arduino PID Library):** Handles the complex calculus required to calculate output based on error over time. - **Thermocouple Interface (MAX6675/MAX31855):** Essential for reading high-precision temperature data from the probe. - **Solid State Relay (SSR):** Allows the low-voltage microcontroller to toggle the high-voltage heating element via Pulse Width Modulation (PWM). - **Web App Integration:** Uses Wi-Fi to transmit real-time telemetry for graphing and data logging. Code Walkthrough: The PID Logic While the MokaBot uses custom software, the logic follows a standard structure. First, we define our constants for **Kp** (Proportional), **Ki** (Integral), and **Kd** (Derivative). ```cpp // Define PID constants double Kp=2.0, Ki=5.0, Kd=1.0; PID myPID(&Input, &Output, &Setpoint, Kp, Ki, Kd, DIRECT); void setup() { Setpoint = 106.0; // Target temperature in Celsius myPID.SetMode(AUTOMATIC); } ``` In the main loop, the controller calculates the error—the difference between the current temperature and the 106-degree target. ```cpp void loop() { Input = readThermocouple(); myPID.Compute(); analogWrite(HEATING_ELEMENT_PIN, Output); } ``` As the temperature nears the set point, the **Output** value decreases, pulsing the heating element to prevent overshooting. This 'judicious application of heat' mimics a skilled human operator but with millisecond-level precision. Syntax Notes and Hardware Conventions In these control systems, **Pulse Width Modulation (PWM)** is the primary convention. Instead of turning a heater 'halfway on' (which is impossible for most elements), the code toggles it on and off rapidly. The ratio of 'on' time to 'off' time determines the effective heat. Additionally, calibration functions are vital. Javi included a boiling-point calibration to ensure accuracy regardless of altitude, a critical feature for global consistency. Practical Examples and Gotchas A common mistake is failing to account for thermal lag. The sensor may report 100 degrees while the element is still radiating heat that will push the water to 110 degrees. This 'overshoot' requires tuning the **Derivative** (Kd) value to predict the rate of change and cut power early. The MokaBot demonstrates this by 'pulsing aggressively' early on and easing off as the coffee begins its steady, non-sputtering flow.