PapAiEra 0.7.0

A Python library for Pulp and Paper manufacturing processes, based on BREF (Best Available Techniques) standards.

PapAiEra

PyPI Project URL: https://pypi.org/project/PapAiEra/

Installation

pip install PapAiEra

PapAiEra is a specialized Python library engineered for the Pulp and Paper industry. It mathematically models and solves optimization problems based on the Best Available Techniques (BAT) reference documents (EU BREF 2015).

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Included Processes & Modules

The library covers a massive array of metrics across the entire pulp and paper production cycle.

1. Pulping Modules (pap_ai_era.pulping)

• Kraft (kraft.py): H-factor calculation, Digester Mass Balance, Kappa numbers, recovery efficiency, black liquor solids, yield, BAT compliance.
• Sulphite (sulphite.py): Yields for paper/dissolving grades, SO2 and Mg/Ca base recovery.
• Mechanical (mechanical.py): Energy intensity of SGW, PGW, TMP, and CTMP, heat recovery.
• Recycled Fibre (recycled.py): Deinking yield, fiber loss, rejects rates.
• Non-Wood (non_wood.py): Depithing efficiency, silica load checks (crucial for bagasse).
• Bleaching Engine (bleaching/): Universal, input-driven bleaching sequence optimizer.
• Adaptive Residual Optimizer: Kappa-less bleaching control using Brightness/Residual signals.
• Hood Optimizer (papermaking/): Dryer hood performance and energy optimizer.
• Bulk Prediction Model (papermaking/): ML-based multi-layer sheet bulk simulator.
• Boiler Optimizer (energy/): Physics-aware Digital Twin and efficiency optimizer.
• SYLVACORE (pulping/sylvacore): Advanced Kraft digestion intelligence (H-factor, Kappa, FurnishLib).
• Recovery Cycle (recovery_cycle.py): Causticizing efficiency, evaporator steam economy, lime kiln energy.

2. Papermaking Modules (pap_ai_era.papermaking)

• Machine Operations (machine.py): Paper machine fiber mass balance, OEE, broke tracking, wire retention, dryer section economy.
• Wet End Chemistry (wet_end_chemistry.py): Furnish-based dosing heuristics, physical GPL to LPH pump conversion algorithms, ash retention.
• Coating/Finishing (coating.py): Colour recovery, ultrafiltration efficiency, specific water usage.
• WetEndChemix (papermaking/wet_end_chemix): Wet-end chemistry, Zeta potential, and fiber bonding simulator.
• Variability Analysis (variability_analysis.py): ABB-compliant VPA engine for MDL, CD, and MDS decomposition.
• DTW Lag Detection (dtw_lag.py): Universal Dynamic Time Warping lag calculator — finds the time delay between ANY input and output process variable.
• Mill Lag Profiles (mill_lag_profiles.py): Pre-built lag analysis scenarios for bleach brightness, tower pH, and viscosity-strength tracking.

3. Sustainability & Carbon (CCTS) (pap_ai_era.sustainability, pap_ai_era.ccts)

• Emissions (emissions.py, air_emissions.py): BOD/COD/TSS load to water. TRS, SO2, NOx, Dust to air.
• Wastewater & Water (wastewater.py, water_energy.py): Loop closure, explicit cooling-water vs contaminated-water accounting.
• Management (EMS): ISO 14001 grading, solid waste footprint (Ash + Sludge + Rejects).
• Power Plant & Cogen (power_plant.py): Boiler thermal efficiency and TG steam tracking.
• CCTS Platform (ccts/): Full No-Code Carbon Credit & Trading System module with Scope 1, 2, 3 carbon calculation, credit estimation, dynamic formulas, and built-in UI.

4. Mathematical AI Solvers (pap_ai_era.optimization_models)

Allows for non-linear optimization using SciPy constraints to determine optimal dosing/setpoints.

• optimize_batch_kraft_digester()
• optimize_continuous_kamyr_digester()
• optimize_bleaching_sequence_cost()
• optimize_sulphite_base_recovery()
• optimize_machine_steam_consumption()
• optimize_wet_end_dosing()
• optimize_cogeneration_tg_fuel()

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Advanced Usage Examples

🧠 Universal Bleaching Optimizer

PapAiEra now includes a Universal Bleaching Brain. Engineers can define custom stages, chemicals, and costs via a simple JSON/dict configuration.

Key Features:

• Dynamic Sequence Building: Supports any number of stages (e.g., D0-EOP-D1, O-Z-P, etc.).
• AI-Driven Simulation: Plug-in system for Heuristic, PNN, or Regression models.
• Global Optimization: Uses Differential Evolution to minimize total chemical cost while hitting Brightness, Kappa, and Viscosity targets.

from pap_ai_era.bleaching import run_optimization_from_config

config = {
    "stages": [
        {
            "name": "D0",
            "chemicals": [{"name": "ClO2", "min_dosage": 5, "max_dosage": 30, "cost": 0.8}]
        },
        {
            "name": "EOP",
            "chemicals": [
                {"name": "NaOH", "min_dosage": 5, "max_dosage": 25, "cost": 0.4},
                {"name": "H2O2", "min_dosage": 2, "max_dosage": 15, "cost": 0.6}
            ]
        }
    ],
    "targets": {"brightness": 90.0, "kappa": 2.0, "viscosity_min": 800}
}
result = run_optimization_from_config(config)

# Full Multi-Stage Optimization (D0-EOP-D1-D2)
config_full = {
    "stages": [
        {"name": "D0", "chemicals": [{"name": "ClO2", "min_dosage": 5, "max_dosage": 25, "cost": 0.8}]},
        {"name": "EOP", "chemicals": [{"name": "NaOH", "min_dosage": 5, "max_dosage": 20, "cost": 0.4}, {"name": "H2O2", "min_dosage": 2, "max_dosage": 10, "cost": 0.6}]},
        {"name": "D1", "chemicals": [{"name": "ClO2", "min_dosage": 2, "max_dosage": 15, "cost": 0.8}]},
        {"name": "D2", "chemicals": [{"name": "ClO2", "min_dosage": 1, "max_dosage": 10, "cost": 0.8}]}
    ],
    "targets": {"brightness": 88.5, "kappa": 1.5}
}
res_full = run_optimization_from_config(config_full)
print(f"Optimal Chemical Cost: ${res_full['best_cost']:.2f}")

📊 Variance Partition Analysis (VPA - ABB 2-Sigma)

Decomposes reel variability into Machine Direction (MDL), Cross Direction (CD), and Residual (MDS) components with automated root-cause inference.

import numpy as np
from pap_ai_era.papermaking.variability_analysis import compute_vpa

# 50 scans and 600 data boxes
data = np.random.normal(loc=50, scale=1.5, size=(50, 600))
vpa_report = compute_vpa(data, process_average=50.0)

print(f"Total 2-Sigma Variability: {vpa_report.normalised['TOT']:.2f}%")
print(f"Primary Problem Detected: {vpa_report.primary_problem}")

🧪 Adaptive Residual Bleaching Optimizer (ARO)

A specialized bleaching controller that eliminates the need for expensive Kappa analyzers. It uses the Optimum Line concept to balance Brightness and Chemical Residual.

Key Features:

• Kappa-Less Control: Operates entirely on final-stage brightness and residual measurements.
• Golden Section Search: High-speed mathematical optimization to find the ideal dosage $Q$.
• Industrial Logic: Built-in deadband ($\epsilon$), rate-of-change limits, and safety overrides.

from pap_ai_era.bleaching import AdaptiveResidualOptimizer

# Setup with target brightness and residual
opt = AdaptiveResidualOptimizer(target_brightness=80.0, target_residual=5.0)

# Detect state and optimize (with rate limiting)
result = opt.optimize(current_b=55.2, current_r=22.5, 
                      simulate_fn=my_sim_fn, current_q=15.0, max_delta=2.0)

📚 ML Bulk Prediction Model

A supervised learning framework to predict paper/board bulk ($cm^3/g$) based on layer-wise furnish, pulp properties (CSF, SW/HW ratio), and pressing conditions.

Key Features:

• Layer-Wise Analysis: Handles multi-layer structures (Top, Middle, Bottom) with independent pulp blends.
• Physics-Aware ML: Combines Random Forest regression with industrial heuristics for robust predictions even with limited data.
• Process Sensitivity: Tracks the impact of Nip Load, Number of Nips, and refining (CSF) on final sheet thickness.

from pap_ai_era.papermaking import BulkModel

# Define layers and process conditions
layers = [{"gsm": 50, "sw_ratio": 0.9, "csf": 550}, {"gsm": 150, "sw_ratio": 0.1, "csf": 400}]
process = {"nip_load": 60, "n_nips": 2}

model = BulkModel(n_layers=2)
# model.train(historical_data) # Train on mill data
predicted_bulk = model.predict(layers, process)

💨 Paper Machine Hood Optimizer

Optimizes the dryer section hood by balancing evaporation load against airflow and recirculation.

Key Features:

• Drying Load Calculation: Uses production rate, speed, and moisture removal (Inlet/Outlet) to determine the exact evaporation demand.
• Energy Minimization: Optimizes Fan RPM and Recirculation to save steam and electrical energy.
• Operational Safety: Ensures the sheet reaches target dryness while minimizing heat loss to the atmosphere.

from pap_ai_era.papermaking import run_hood_optimization

params = {
    "machine_speed": 1200, "gsm": 80, "width": 6.5,
    "inlet_moisture": 42.0, "target_moisture": 5.0,
    "return_temp": 85.0, "outside_temp": 30.0, "humidity": 0.15
}
best = run_hood_optimization(params)

🔥 Universal Boiler Optimizer

A physics-aware Boiler Digital Twin that uses hybrid modeling to maximize thermal efficiency while staying within safety and emission constraints.

Key Features:

• Hybrid Modeling: Combines first-principles energy balance with ML residuals.
• Efficiency Maximization: Finds the sweet spot for fuel/air ratios to minimize stack loss and unburnt fuel.
• Constraint Safety: Automatically respects limits for O2%, steam pressure, and flue gas temperature.

from pap_ai_era.energy.boiler_optimizer import Boiler, BoilerOptimizer

config = {"boiler_type": "AFBC", "fuel": {"CV": 4200}}
boiler = Boiler(config)
optimizer = BoilerOptimizer(boiler)

# Optimize for maximum efficiency
result = optimizer.optimize(disturbances={"feedwater_temp": 110})

⚙️ Kraft Digester Vroom H-Factor

Calculates the integrated cooking reaction rate required to hit your Kappa target.

from pap_ai_era.pulping.kraft import calculate_h_factor

# 165 °C cooking zone for 45 minutes
h_factor_generated = calculate_h_factor(temperature_celsius=165.0, time_minutes=45.0)
print(f"H-Factor Generated: {h_factor_generated:.2f}")

💧 Papermaking Moisture Prediction

Simulates the entire water loss journey through the Wire formers, Press Nips, and Dryers.

from pap_ai_era.papermaking.moisture_prediction import predict_wire_drainage_and_couch_moisture

couch = predict_wire_drainage_and_couch_moisture(
    target_gsm=80, machine_speed_mpm=1200, wire_width_m=6.5,
    layer_data=[{'gsm': 40}, {'gsm': 40}],
    vacuum_boxes=[{'vacuum_kpa': 15}, {'vacuum_kpa': 35}, {'vacuum_kpa': 60}]
)
print(f"Moisture exiting Couch Roll: {couch['moisture_after_couch_pct']:.2f}%")

🌲 SYLVACORE - Digester Process Intelligence

A systematic framework for Kraft pulping that models furnish reactivity, dynamic H-factors, and stage-wise delignification.

Key Features:

• FurnishLib: Comprehensive reference for Softwood, Hardwood, and Non-wood species.
• Dynamic H-Factor: Furnish-corrected H-factor calculations ($H_{eff} = H \times R_f$).
• Impregnation Quality (IQI): Real-time tracking of liquor penetration effectiveness.
• Batch Digester Identity (BDI): Lifecycle tracking and vessel-specific bias correction.

from pap_ai_era.pulping.sylvacore import FurnishLib, HFactorEngine, ChemDosing

flib = FurnishLib()
h_eng = HFactorEngine()
d_eng = ChemDosing()

# Optimize dosing for Eucalyptus
furnish = flib.get_furnish("HW_EUC_GLOB")
dosing = d_eng.calculate_optimal_dosing(furnish, kappa_target=16.0, moisture_pct=50.0)

# Calculate Effective H-Factor from profile
profile = [(0, 80), (60, 160), (120, 160)]
h_eff = h_eng.get_effective_h(h_eng.calculate_raw_h(profile), furnish)

⚗️ WetEndChemix - Chemistry & Bonding

A specialized simulator to "see the invisible" wet-end chemistry. It predicts charge balance, Zeta potential, and the resulting Fiber Bonding Index (FBI) to minimize paper break risks.

Key Features:

• ZetaPredictor: Hybrid Stern-Debye model for colloidal charge stability.
• BondStrengthModel: Predicts break probability based on chemistry, refining, and starch dosing.
• Shop-Floor Visualizer: Optional PyQt6-based dashboard for real-time monitoring.

from pap_ai_era.papermaking.wet_end_chemix import WetEndSimulator, ChemicalDatabase

db = ChemicalDatabase()
sim = WetEndSimulator(db)

# Simulate current mill state
results = sim.run_simulation(
    dosages={"CPAM": 0.25, "STARCH": 12.0},
    ph=6.8, temp_C=48.0, 
    refining_energy=80.0, gsm=70.0
)
print(f"Break Risk: {results['break_risk_pct']:.1f}%")

⏱️ DTW Lag Detection — Universal Time Delay Finder

Finds the time lag between ANY input parameter and ANY output parameter using Dynamic Time Warping. Works for pulp mills, chemical plants, power plants — any process where a cause variable affects an effect variable with a delay.

Key Features:

• Universal: Works with any two time-series signals — no domain configuration needed.
• Dynamic Time Warping: Superior to simple cross-correlation for noisy, non-stationary signals.
• Handles Inverse Relationships: Detects lag even when cause and effect are inversely correlated (e.g., temperature ↑ → kappa ↓).
• Multi-Variable Audit: Test all input-output combinations in one call.
• Pre-Built Mill Scenarios: Ready-to-use configurations for common pulp & paper lag analysis.
• Confidence Scoring: Rates each result as HIGH, MEDIUM, or LOW confidence.

Use Case 1: Simple — Find lag between any two signals

from pap_ai_era.papermaking import find_lag

# Any input array and output array — that's all you need
result = find_lag(
    input_signal=temperature_data,
    output_signal=viscosity_data,
    time_interval=1.0,       # 1 sample per minute
    time_unit='minutes'
)

print(f"Lag: {result['lag_time']} {result['lag_unit']}")
print(f"Confidence: {result['confidence']}")

Use Case 2: Digester cook temperature → Kappa number

from pap_ai_era.papermaking import find_lag

# Real DCS historian data (1-minute intervals)
result = find_lag(
    input_signal=cook_temperature,
    output_signal=kappa_number,
    time_interval=1.0,
    time_unit='minutes',
    max_lag=80,
    input_name='Cook Temperature',
    output_name='Kappa Number'
)
print(f"Cook-to-Kappa delay: {result['lag_time']:.1f} minutes")
# Use this lag for feedforward dead-time compensation in DCS

Use Case 3: Multi-variable process audit from a DataFrame

import pandas as pd
from pap_ai_era.papermaking import find_multi_lag

# Load your process historian data
df = pd.read_csv('process_data.csv')

# Test ALL input-output combinations at once
results = find_multi_lag(
    data=df,
    input_columns=['consistency', 'headbox_pressure', 'steam_flow', 'refiner_energy'],
    output_columns=['basis_weight', 'moisture', 'caliper', 'tensile_strength'],
    time_interval=1.0,
    time_unit='minutes'
)
print(results)
#          Input         Output  Lag (minutes)  Confidence  Correlation
#    consistency   basis_weight           45.0        HIGH       0.9310
#     steam_flow       moisture           83.0        HIGH       0.4158
# refiner_energy        caliper           12.0      MEDIUM       0.8500

Use Case 4: Pre-built mill scenario — Bleach tower brightness to board brightness

from pap_ai_era.papermaking import MillScenario, run_mill_scenario

result = run_mill_scenario(
    MillScenario.BLEACH_BRIGHTNESS,
    cause=bleach_tower_inlet_brightness,
    effect=board_brightness_qcs
)
print(result.summary())
# === Bleach Tower Inlet Brightness to Board Brightness ===
# DTW Optimal Lag:    122.0 min
# Confidence:         HIGH
# Status: NORMAL
# Interpretation:
#   - Lag of 122.0 min is within expected range (60-240 min)
#   - Use this lag for feedforward dead-time compensation in DCS

Use Case 5: Tower pH → Wet-end pH

from pap_ai_era.papermaking import MillScenario, run_mill_scenario

result = run_mill_scenario(
    MillScenario.TOWER_PH_WETEND,
    cause=bleach_tower_exit_ph,
    effect=wet_end_ph
)
print(f"pH propagation delay: {result.dtw_result.optimal_lag_seconds / 60:.0f} minutes")

Use Case 6: Stage-wise viscosity → Strength properties (tensile, burst, tear)

from pap_ai_era.papermaking import run_viscosity_strength_audit

report = run_viscosity_strength_audit(
    viscosity_signals={
        'D0_viscosity': d0_visc_data,
        'EOP_viscosity': eop_visc_data,
        'D1_viscosity': d1_visc_data,
    },
    strength_signals={
        'tensile_index': tensile_data,
        'burst_index': burst_data,
        'tear_index': tear_data,
    }
)
print(report.summary_table)
# Shows which bleach stage most impacts which strength property

Use Case 7: Non-pulp example — Any chemical/industrial process

from pap_ai_era.papermaking import find_lag

# Reactor feed rate → product concentration
result = find_lag(feed_rate, product_concentration,
                  time_interval=5.0, time_unit='seconds')

# Boiler fuel flow → steam header pressure
result = find_lag(fuel_flow, steam_pressure,
                  time_interval=1.0, time_unit='seconds')

# Cooling water temperature → heat exchanger outlet
result = find_lag(cw_inlet_temp, hx_outlet_temp,
                  time_interval=10.0, time_unit='seconds')

Use Case 8: List all available mill scenarios

from pap_ai_era.papermaking import list_scenarios
print(list_scenarios())
#                              Scenario  Expected Lag       Process Area
#   bleach_tower_inlet_brightness_to_...   60-240 min  Bleach Plant to PM
#   bleach_tower_ph_to_wetend_ph          30-180 min   Washers to Wet-End
#   stagewise_viscosity_to_strength_...  120-480 min   Bleach to QC Lab

🌍 Carbon Credit & Trading System (CCTS)

A complete module for No-Code Carbon Calculation, Carbon Credit Estimation, and ESG reporting for Pulp, Paper, Board & Packaging mills. Features editable emission factors (IPCC/IEA/CEA), product grade baseline comparison, and a built-in Streamlit UI.

Key Features:

• Full Scope Calculation: Tracks Scope 1 (Direct), Scope 2 (Energy), and Scope 3 (Value Chain) emissions.
• Credit Estimator: Estimate carbon credits from fuel switching or efficiency improvements.
• Editable Factors: Easily update Fuel, Grid Electricity, Steam, Process, and Fiber emission factors via Python or Excel.
• No-Code Dashboard: Launch the built-in UI for drag-and-drop analytics.

Use Case 1: Carbon Calculator (Code)

from pap_ai_era.ccts import CarbonCalculator
calc = CarbonCalculator()
result = calc.calculate(
    product='kraft_liner',
    production_tons=1000,
    fuel={'coal_bituminous': 5000, 'natural_gas': 2000},
    electricity_mwh=550,
    electricity_region='india_national'
)
print(result.summary())

Use Case 2: Carbon Credit Estimation

from pap_ai_era.ccts import CreditEstimator
ce = CreditEstimator(price_per_ton=25.0)
result = ce.from_fuel_switch(
    project_name="Coal to Biomass Conversion",
    production_tons=50000,
    baseline_fuel={'coal_bituminous': 200000},
    proposed_fuel={'biomass_wood': 180000, 'natural_gas': 30000}
)
print(result.summary())

Use Case 3: Launching the No-Code UI

python -m pap_ai_era.ccts.ui.app

This opens a browser-based Streamlit dashboard where users can upload Excel data, edit emission factors, and build custom ESG KPI dashboards without writing any code.

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Troubleshooting Guide

1. Optimization returns success: False: Target constraints are mathematically impossible. Adjust boundaries in the bounds variables. 2. Import Errors: Ensure you run pip install PapAiEra in an active environment. 3. Data Shape for VPA: The VPA engine expects a 2D numpy array of (scans, data_boxes).