pH and Alkalinity Control System

Comprehensive pH control with gain scheduling, carbonate equilibrium, alkalinity tracking, and struvite monitoring (Task 189)

Overview

The pH and Alkalinity Control System provides advanced chemistry control for wastewater treatment, enabling precise pH management while tracking alkalinity consumption and monitoring struvite precipitation risk. The system is critical for:

Maintaining optimal conditions for biological treatment (pH 6.5-8.5)
Preventing nitrification inhibition from low alkalinity
Avoiding struvite scaling in anaerobic digestion and sidestream treatment
Optimizing chemical dosing for cost-effective operation

Services

Service	Purpose
`CarbonateEquilibrium`	pH calculation from alkalinity/TIC using Henderson-Hasselbalch
`AlkalinityTrackingService`	Mass balance of alkalinity consumption and production
`pHController`	PID control with gain scheduling for acid/base dosing
`StruviteMonitor`	Saturation index calculation and precipitation risk assessment

Carbonate Equilibrium System

Henderson-Hasselbalch Equation

The carbonate system is the primary buffer in wastewater. The pH is calculated from the equilibrium:

$$\text{H}_2\text{CO}_3^* \rightleftharpoons \text{HCO}3^- + \text{H}^+ \quad (pK)$$

$$\text{HCO}_3^- \rightleftharpoons \text{CO}3^{2-} + \text{H}^+ \quad (pK)$$

Where H2CO3* represents dissolved CO2 plus carbonic acid.

Equilibrium Constants

Constant	Value at 25C	Description
pKa1	6.35	First dissociation (H2CO3 to HCO3-)
pKa2	10.33	Second dissociation (HCO3- to CO3^2-)
KH_CO2	3.388 x 10^-2 mol/L/atm	Henry's law constant
pCO2_atm	4.0 x 10^-4 atm	Atmospheric CO2

Temperature Corrections

Equilibrium constants are temperature-dependent:

pKa1(T) = pKa1(25) + (-0.0108) x (T - 25)
pKa2(T) = pKa2(25) + (-0.0116) x (T - 25)

Temperature (C)	pKa1	pKa2
10	6.51	10.50
15	6.46	10.45
20	6.40	10.39
25	6.35	10.33
30	6.30	10.27

Alpha Values (Carbonate Speciation)

The distribution of carbonate species is described by alpha values:

$$\alpha_0 = \frac{[\text{H}^+]^2}{[\text{H}^+]^2 + [\text{H}^+]K_1 + K_1K_2}$$

$$\alpha_1 = \frac{[\text{H}^+]K_1}{[\text{H}^+]^2 + [\text{H}^+]K_1 + K_1K_2}$$

$$\alpha_2 = \frac{K_1K_2}{[\text{H}^+]^2 + [\text{H}^+]K_1 + K_1K_2}$$

Where:

alpha_0 = fraction as H2CO3* (dissolved CO2)
alpha_1 = fraction as HCO3- (bicarbonate)
alpha_2 = fraction as CO3^2- (carbonate)

mermaid

xychart-beta
    title "Carbonate Speciation vs pH"
    x-axis "pH" 4 --> 12
    y-axis "Fraction (alpha)" 0 --> 1
    line "H2CO3* (alpha_0)" [1.0, 0.98, 0.88, 0.50, 0.10, 0.01, 0.001, 0.0001, 0.0]
    line "HCO3- (alpha_1)" [0.0, 0.02, 0.12, 0.50, 0.90, 0.98, 0.88, 0.50, 0.10]
    line "CO3^2- (alpha_2)" [0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.12, 0.50, 0.90]

Buffer Intensity

Buffer intensity (beta) measures resistance to pH change:

$$\beta = 2.303 \times \left( C_T \times (\alpha_0\alpha_1 + 4\alpha_1\alpha_2) + [\text{H}^+] + [\text{OH}^-] \right)$$

pH Range	Buffer Intensity	Typical Wastewater
< 5.5	Low (H+ dominated)	Unstable
5.5 - 7.5	High (carbonate)	Well buffered
7.5 - 9.5	Moderate	Stable
> 9.5	Low (OH- dominated)	Unstable

Usage

typescript

import {
  calculateCarbonateEquilibrium,
  calculateAlphaValues,
  calculateBufferIntensity,
  calculatepKa1,
  calculatepKa2,
} from '@/flow/services/simulation/chemistry/carbonateEquilibrium';

// Calculate pH from alkalinity and TIC
const result = calculateCarbonateEquilibrium({
  totalAlkalinity: 200,      // mg/L as CaCO3
  totalInorganicCarbon: 50,  // mg/L as C
  temperature: 20,           // degrees C
  ionicStrength: 0.01,       // mol/L
  atmosphericCO2: 4.0e-4,    // atm
  closedSystem: false,       // open to atmosphere
});

console.log(result.pH);              // Calculated pH
console.log(result.speciation);      // H2CO3, HCO3, CO3 concentrations
console.log(result.bufferIntensity); // meq/L/pH
console.log(result.LSI);             // Langelier Saturation Index

// Get alpha values directly
const [alpha0, alpha1, alpha2] = calculateAlphaValues(7.0, 6.35, 10.33);

Alkalinity Tracking

Mass Balance Approach

Alkalinity changes through biological and chemical processes are tracked using stoichiometric factors:

mermaid

flowchart LR
    INF[Influent<br>Alkalinity] --> BAL((Mass<br>Balance))
    NITR[Nitrification<br>-7.14 g/g N] --> BAL
    DENITR[Denitrification<br>+3.57 g/g N] --> BAL
    CHEM[Chemical<br>Dosing] --> BAL
    PREM[P Removal<br>-0.5 to 5.5 g/g P] --> BAL
    FERM[Fermentation<br>+0.07 g/g COD] --> BAL
    BAL --> EFF[Effluent<br>Alkalinity]

Stoichiometric Factors

Process	Factor (g CaCO3/g)	Notes
Nitrification	-7.14 per g NH4-N	Consumes alkalinity
Denitrification	+3.57 per g NO3-N	Produces alkalinity
Biological P Removal	-0.5 per g P	Minor consumption
Alum P Removal	-5.5 per g P	Major consumption
Ferric P Removal	-4.9 per g P	Major consumption
Fermentation	+0.07 per g COD	VFA production
Lime Softening	-2.5 per g Ca	Hardness removal

Alkalinity Balance Calculation

typescript

import {
  AlkalinityTrackingService,
  calculateAlkalinityBalance,
} from '@/flow/services/simulation/chemistry/alkalinityTrackingService';

const service = new AlkalinityTrackingService({
  influentAlkalinity: 250,    // mg/L as CaCO3
  nitrificationRate: 50,      // kg N/d
  denitrificationRate: 30,    // kg N/d
  chemicalDosing: [
    {
      reagent: 'NaOH',
      doseRate: 10,           // L/h
      unit: 'L/h',
      location: 'secondary',
    },
  ],
  biologicalPRemoval: 5,      // kg P/d
  chemicalPRemoval: 0,        // kg P/d
  fermentationRate: 100,      // kg COD/d
  effluentFlow: 10000,        // m3/d
}, {
  setpoint: 150,              // mg/L target
  lowAlarm: 100,              // mg/L low alarm
  highAlarm: 300,             // mg/L high alarm
});

// Update with process data
const result = service.update({
  nitrificationRate: 55,      // Changed nitrification
});

console.log(result.alkalinity);       // Effluent alkalinity (mg/L)
console.log(result.balance);          // Detailed mass balance
console.log(result.trend);            // 'increasing', 'stable', 'decreasing'
console.log(result.alarmState);       // 'normal', 'low', 'high', 'critical'
console.log(result.timeToCritical);   // Hours until critical (if decreasing)
console.log(result.doseAdjustment);   // Recommended dose change (%)

Alarm Thresholds

Level	Threshold (mg/L CaCO3)	Action
Critical	<= 50	Immediate supplementation required
Low	<= 100	Increase chemical dosing
Normal	100 - 400	No action needed
High	>= 400	Reduce chemical dosing

Required NaOH Dose Calculation

typescript

import { calculateRequiredNaOHDose } from '@/flow/services/simulation/chemistry/alkalinityTrackingService';

const naohDose = calculateRequiredNaOHDose(
  50,    // nitrificationRate (kg N/d)
  30,    // denitrificationRate (kg N/d)
  150,   // targetAlkalinity (mg/L)
  200,   // influentAlkalinity (mg/L)
  10000  // effluentFlow (m3/d)
);

console.log(`Required 50% NaOH: ${naohDose.toFixed(1)} L/h`);

pH Control with Gain Scheduling

Why Gain Scheduling?

The titration curve for wastewater is highly nonlinear. Near neutral pH, small reagent additions cause large pH changes, while at extreme pH values, much larger doses are needed. Gain scheduling compensates for this nonlinearity.

mermaid

xychart-beta
    title "pH Titration Curve (Typical Wastewater)"
    x-axis "Base Added (meq/L)" -10 --> 10
    y-axis "pH" 2 --> 12
    line "Titration Curve" [2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5]

PID Controller with Anti-Windup

The controller uses a PID algorithm with back-calculation anti-windup:

$$u(t) = K_p \cdot e(t) + K_i \int e(t)dt + K_d \frac{de(t)}{dt}$$

Where:

u(t) = controller output
e(t) = pH error (setpoint - measurement)
Kp, Ki, Kd = proportional, integral, derivative gains

Gain Scheduling Table

pH Range	Gain Multiplier	Reason
0 - 4	0.1	Strong acid region - slow response
4 - 5	0.3	Weak acid region
5 - 6	0.6	Approaching neutral
6 - 6.5	1.0	Moderate buffer
6.5 - 7.5	2.0	Minimum buffer - maximum sensitivity
7.5 - 8	1.0	Moderate buffer
8 - 9	0.6	Approaching basic
9 - 10	0.3	Weak base region
10 - 14	0.1	Strong base region - slow response

Control Cascade Structure

mermaid

flowchart LR
    SP[pH Setpoint] --> E1((+/-))
    PV[pH Measured] --> E1
    E1 --> ERROR[Error]
    ERROR --> SCHED[Gain<br>Scheduler]
    SCHED --> MULT[Multiplier]
    MULT --> PID[PID<br>Controller]
    PID --> OUTPUT[Output<br>-100 to +100]
    OUTPUT --> SPLIT{Split<br>Range}
    SPLIT -->|< 0| ACID[Acid<br>Pump]
    SPLIT -->|> 0| BASE[Base<br>Pump]

Split-Range Control

Controller output is split between acid and base:

Output Range	Acid Dose	Base Dose
-100 to -5	0-100%	0%
-5 to +5	0% (deadband)	0%
+5 to +100	0%	0-100%

Configuration

typescript

import {
  pHController,
  createpHController,
  createDefaultGainSchedule,
} from '@/flow/services/simulation/chemistry/pHController';

const controller = createpHController({
  id: 'ph-control-1',
  name: 'Neutralization pH Control',
  enabled: true,
  pHsetpoint: 7.0,
  pHdeadband: 0.1,
  controllerGains: {
    Kp: 10,
    Ki: 1,
    Kd: 0.5,
  },
  gainScheduling: createDefaultGainSchedule(),
  acidReagent: 'H2SO4',      // 93% sulfuric acid
  baseReagent: 'NaOH',       // 50% sodium hydroxide
  neutralizationVolume: 100, // m3
  mixingEfficiency: 0.85,
  maxAcidDose: 100,          // L/h
  maxBaseDose: 100,          // L/h
  responseTime: 60,          // seconds
  measurementNodeId: 'effluent-ph',
  actuatorNodeId: 'reagent-pump',
});

// Execute control calculation
const measurement = {
  locationId: 'effluent-ph',
  pH: 6.5,
  temperature: 20,
  timestamp: Date.now(),
  quality: 'good',
};

const output = controller.calculate(measurement, 0.001, 50); // dt in days, TIC in mg/L

console.log(output.output);        // Controller output (-100 to +100)
console.log(output.acidDoseRate);  // L/h acid
console.log(output.baseDoseRate);  // L/h base
console.log(output.gainMultiplier); // Current gain multiplier
console.log(output.mode);          // 'auto', 'manual', 'cascade'

Reagent Properties

Reagent	Formula	Concentration	Density (kg/L)	Alkalinity Factor
Sodium Hydroxide	NaOH	50%	1.53	+1.25 g CaCO3/g
Calcium Hydroxide	Ca(OH)2	15% slurry	1.20	+1.35 g CaCO3/g
Sodium Carbonate	Na2CO3	10%	1.10	+0.94 g CaCO3/g
Magnesium Hydroxide	Mg(OH)2	60% slurry	1.35	+1.72 g CaCO3/g
Sulfuric Acid	H2SO4	93%	1.83	-1.02 g CaCO3/g
Hydrochloric Acid	HCl	37%	1.19	-1.37 g CaCO3/g
Carbon Dioxide	CO2	100%	0.002	-2.27 g CaCO3/g

Tuning Guidelines

Parameter	Typical Range	Notes
Kp	5 - 20	Start at 10, adjust based on response
Ki	0.5 - 2	Keep low to prevent oscillation
Kd	0 - 1	Often not needed, use sparingly
Deadband	0.05 - 0.2	Prevents hunting around setpoint
Response Time	30 - 120 s	System lag compensation

Struvite Saturation Monitoring

Precipitation Chemistry

Struvite (MgNH4PO4 . 6H2O) precipitates when the solution is supersaturated:

$$\text{Mg}^{2+} + \text{NH}_4^+ + \text{PO}_4^{3-} + 6\text{H}_2\text{O} \rightarrow \text{MgNH}_4\text{PO}_4 \cdot 6\text{H}_2\text{O}$$

Saturation Index Calculation

$$\text{SI} = \log_{10}\left(\frac{\text{IAP}}{K_{sp}}\right)$$

Where:

IAP = Ion Activity Product = {Mg2+} x {NH4+} x
Ksp = Solubility product (10^-13.26 at 25C)

SI Value	Condition	Risk Level
< 0	Undersaturated	Low
0 - 0.5	Near saturation	Moderate
0.5 - 1.0	Supersaturated	High
> 1.0	Highly supersaturated	Critical

Risk Zones Diagram

mermaid

quadrantChart
    title Struvite Precipitation Risk vs pH and PO4
    x-axis "Low PO4" --> "High PO4"
    y-axis "Low pH (6)" --> "High pH (9)"
    quadrant-1 "HIGH RISK"
    quadrant-2 "MODERATE RISK"
    quadrant-3 "LOW RISK"
    quadrant-4 "MODERATE RISK"

Ion Activity Calculations

Activity coefficients are calculated using the Davies equation:

$$\log(\gamma) = -A \times z^2 \times \left(\frac{\sqrt{I}}{1 + \sqrt{I}} - 0.3I\right)$$

Where:

A approximately equals 0.5 at 25C
z = ion charge
I = ionic strength (mol/L)

Struvite Monitor Usage

typescript

import {
  StruviteMonitor,
  calculateStruviteSaturation,
  calculateSaturationIndex,
  estimateMinimumpH,
} from '@/flow/services/simulation/chemistry/struviteMonitor';

const monitor = new StruviteMonitor({
  magnesium: 25,      // mg/L as Mg
  ammonia: 100,       // mg/L as N
  phosphate: 20,      // mg/L as P
  temperature: 35,    // degrees C (typical digester)
  pH: 7.5,
  ionicStrength: 0.02,
}, {
  alarmThreshold: 0.5,
  monitoringLocations: ['digester_effluent', 'centrate'],
});

// Update with new measurements
const result = monitor.update({
  pH: 7.8,            // pH increased
  phosphate: 25,      // PO4 increased
});

console.log(result.saturationIndex);        // SI value
console.log(result.riskLevel);              // 'low', 'moderate', 'high', 'critical'
console.log(result.supersaturationRatio);   // IAP/Ksp
console.log(result.limitingIon);            // 'Mg', 'NH4', or 'PO4'
console.log(result.precipitationPotential); // mg/L as struvite
console.log(result.recommendations);        // Array of action items

// Calculate safe pH
const safepH = estimateMinimumpH(25, 100, 20, 35, 0.02);
console.log(`Keep pH below ${safepH.toFixed(1)} to prevent struvite`);

// Calculate target pH for SI = 0
const targetpH = monitor.calculateRequiredpHForTargetSI(0);
console.log(`Target pH: ${targetpH.toFixed(2)}`);

Prevention Strategies

Strategy	Implementation	Effectiveness
pH Control	Maintain pH < 7.0	High
P Removal	Chemical precipitation upstream	High
Mg Reduction	Limit Mg sources	Moderate
Dilution	Increase flow	Low
Controlled Crystallization	Struvite reactor	Best
Anti-scalants	Chemical addition	Moderate

Struvite Recovery Potential

typescript

import { calculateStruviteRecoveryPotential } from '@/flow/services/simulation/chemistry/struviteMonitor';

const params = {
  magnesium: 25,
  ammonia: 100,
  phosphate: 20,
  temperature: 35,
  pH: 8.0,
  ionicStrength: 0.02,
};

const recoveryKgPerDay = calculateStruviteRecoveryPotential(params, 500); // 500 m3/d
console.log(`Potential struvite recovery: ${recoveryKgPerDay.toFixed(1)} kg/d`);

Configuration Examples

Complete pH Control Configuration

json

{
  "id": "neutralization-tank-1",
  "name": "Primary Neutralization",
  "enabled": true,
  "pHsetpoint": 7.2,
  "pHdeadband": 0.15,
  "controllerGains": {
    "Kp": 12,
    "Ki": 0.8,
    "Kd": 0.3
  },
  "gainScheduling": [
    { "pHmin": 0, "pHmax": 5, "gainMultiplier": 0.2 },
    { "pHmin": 5, "pHmax": 6, "gainMultiplier": 0.5 },
    { "pHmin": 6, "pHmax": 6.5, "gainMultiplier": 1.0 },
    { "pHmin": 6.5, "pHmax": 7.5, "gainMultiplier": 2.0 },
    { "pHmin": 7.5, "pHmax": 8, "gainMultiplier": 1.0 },
    { "pHmin": 8, "pHmax": 9, "gainMultiplier": 0.5 },
    { "pHmin": 9, "pHmax": 14, "gainMultiplier": 0.2 }
  ],
  "acidReagent": "H2SO4",
  "baseReagent": "NaOH",
  "neutralizationVolume": 150,
  "mixingEfficiency": 0.9,
  "maxAcidDose": 150,
  "maxBaseDose": 200,
  "responseTime": 45,
  "measurementNodeId": "tank1-ph-sensor",
  "actuatorNodeId": "reagent-pumps"
}

Alkalinity Tracking Configuration

json

{
  "influentAlkalinity": 280,
  "nitrificationRate": 75,
  "denitrificationRate": 45,
  "chemicalDosing": [
    {
      "reagent": "NaOH",
      "doseRate": 15,
      "unit": "L/h",
      "location": "activated_sludge"
    },
    {
      "reagent": "Na2CO3",
      "doseRate": 50,
      "unit": "kg/h",
      "location": "primary"
    }
  ],
  "biologicalPRemoval": 8,
  "chemicalPRemoval": 12,
  "fermentationRate": 200,
  "effluentFlow": 25000,
  "setpoint": 180,
  "lowAlarm": 100,
  "highAlarm": 350
}

Struvite Monitor Configuration

json

{
  "magnesium": 30,
  "ammonia": 150,
  "phosphate": 35,
  "temperature": 37,
  "pH": 7.3,
  "ionicStrength": 0.025,
  "alarmThreshold": 0.5,
  "monitoringLocations": [
    "digester_effluent",
    "centrate_return",
    "thickener_overflow"
  ]
}

Integration with ErgoPlanner

Adding pH Controllers to Simulations

Create Chemistry Service

typescript

import { ChemistryControlService } from '@/flow/services/simulation/chemistry';

const chemService = new ChemistryControlService();

Register Controllers

typescript

chemService.registerController(pHController);
chemService.registerAlkalinityTracker(alkalinityService);
chemService.registerStruviteMonitor(struviteMonitor);

Connect to Simulation Engine

typescript

simulationEngine.addService('chemistry', chemService);

Measurement Node Configuration

pH measurement nodes should be configured with:

Property	Description	Typical Value
locationId	Unique identifier	'reactor-effluent'
sampleInterval	Measurement frequency	1 minute
sensorLag	Sensor response time	30 seconds
noise	Measurement noise	0.05 pH units
bias	Calibration offset	0

Controller Output Integration

Controller outputs are applied to dosing pumps:

typescript

// In simulation step
const chemResult = chemService.execute(context);

for (const output of chemResult.pHControlOutputs) {
  const pump = nodes.get(output.targetNodeId);
  if (pump) {
    pump.setAcidFlow(output.acidDoseRate);
    pump.setBaseFlow(output.baseDoseRate);
  }
}

Best Practices

Tuning Order: Start with Kp only, add Ki slowly, use Kd sparingly
Gain Schedule: Calibrate based on actual titration curve
Sensor Placement: Install pH sensors in well-mixed zones
Redundancy: Use multiple sensors for critical applications
Calibration: Weekly calibration recommended
Alkalinity Monitoring: Track trends, not just absolute values
Struvite Prevention: Monitor SI at high-risk locations daily

Cascade-Controller - Multi-loop control strategies
Aeration-Control - DO control integration
Overview - Dosing pump configuration
Nitrogen-Balance - Nitrification alkalinity consumption

Source Files

File	Description
`flow/services/simulation/chemistry/types.ts`	Type definitions and constants
`flow/services/simulation/chemistry/carbonateEquilibrium.ts`	Carbonate system calculations
`flow/services/simulation/chemistry/alkalinityTrackingService.ts`	Alkalinity mass balance
`flow/services/simulation/chemistry/pHController.ts`	pH control with gain scheduling
`flow/services/simulation/chemistry/struviteMonitor.ts`	Struvite saturation monitoring

References

Metcalf & Eddy (2014). "Wastewater Engineering" Chapter 3: Wastewater Chemistry
Snoeyink, V.L. & Jenkins, D. (1980). "Water Chemistry"
WEF MOP 8 (2009). "Design of Municipal Wastewater Treatment Plants"
Ohlinger, K.N. et al. (1998). "Predicting struvite formation in digestion"
Henze, M. et al. (2008). "Biological Wastewater Treatment: Principles, Modelling and Design"

Clarification

Disinfection

Filtration

Membranes

pH and Alkalinity Control System ​

Overview ​

Services ​

Carbonate Equilibrium System ​

Henderson-Hasselbalch Equation ​

Equilibrium Constants ​

Temperature Corrections ​

Alpha Values (Carbonate Speciation) ​

Buffer Intensity ​

Usage ​

Alkalinity Tracking ​

Mass Balance Approach ​

Stoichiometric Factors ​

Alkalinity Balance Calculation ​

Alarm Thresholds ​

Required NaOH Dose Calculation ​

pH Control with Gain Scheduling ​

Why Gain Scheduling? ​

PID Controller with Anti-Windup ​

Gain Scheduling Table ​

Control Cascade Structure ​

Split-Range Control ​

Configuration ​

Reagent Properties ​

Tuning Guidelines ​

Struvite Saturation Monitoring ​

Precipitation Chemistry ​

Saturation Index Calculation ​

Risk Zones Diagram ​

Ion Activity Calculations ​

Struvite Monitor Usage ​

Prevention Strategies ​

Struvite Recovery Potential ​

Configuration Examples ​

Complete pH Control Configuration ​

Alkalinity Tracking Configuration ​

Struvite Monitor Configuration ​

Integration with ErgoPlanner ​

Adding pH Controllers to Simulations ​

Measurement Node Configuration ​

Controller Output Integration ​

Best Practices ​

Related Topics ​

Source Files ​

References ​

pH and Alkalinity Control System

Overview

Services

Carbonate Equilibrium System

Henderson-Hasselbalch Equation

Equilibrium Constants

Temperature Corrections

Alpha Values (Carbonate Speciation)

Buffer Intensity

Usage

Alkalinity Tracking

Mass Balance Approach

Stoichiometric Factors

Alkalinity Balance Calculation

Alarm Thresholds

Required NaOH Dose Calculation

pH Control with Gain Scheduling

Why Gain Scheduling?

PID Controller with Anti-Windup

Gain Scheduling Table

Control Cascade Structure

Split-Range Control

Configuration

Reagent Properties

Tuning Guidelines

Struvite Saturation Monitoring

Precipitation Chemistry

Saturation Index Calculation

Risk Zones Diagram

Ion Activity Calculations

Struvite Monitor Usage

Prevention Strategies

Struvite Recovery Potential

Configuration Examples

Complete pH Control Configuration

Alkalinity Tracking Configuration

Struvite Monitor Configuration

Integration with ErgoPlanner

Adding pH Controllers to Simulations

Measurement Node Configuration

Controller Output Integration

Best Practices

Related Topics

Source Files

References