electricsheepafrica/african-cement-plant-operations

---
title: African Cement Plant Operations
license: cc-by-4.0
language: en
task_categories:
- time-series-forecasting
- regression
- classification
tags:
- cement
- manufacturing
- africa
- energy
- emissions
- industrial-operations
- sustainability
configs:
- config_name: baseline
data_files: baseline.csv
description: Normal operations reflecting current African cement industry conditions (2024-2025)
- config_name: energy_crisis
data_files: energy_crisis.csv
description: Energy supply disruptions, fuel price spikes, grid instability (modelled on South Africa load-shedding, Nigeria gas shortages)
- config_name: green_transition
data_files: green_transition.csv
description: Accelerated adoption of alternative fuels, energy efficiency upgrades, carbon reduction initiatives aligned with GCCA 2050 net-zero roadmap
---

# African Cement Plant Operations Dataset

Comprehensive dataset of cement plant operations across **12 African countries** with **3 operational scenarios**, covering **2020-2025** at monthly granularity.

## Overview

| Statistic | Value |
|---|---|
| Total Records | 21,384 |
| Countries | 12 |
| Plants | 99 |
| Scenarios | 3 |
| Time Range | January 2020 - December 2025 |
| Features per Record | 30 |

## Countries Covered

| Country | Region | Installed Capacity (Mt/yr) | Plants |
|---|---|---|---|
| Egypt | North Africa | 90.0 | 22 |
| Nigeria | West Africa | 52.0 | 14 |
| Algeria | North Africa | 28.0 | 12 |
| Morocco | North Africa | 22.0 | 7 |
| South Africa | Southern Africa | 16.5 | 8 |
| Ethiopia | East Africa | 18.0 | 9 |
| Kenya | East Africa | 12.0 | 6 |
| Tanzania | East Africa | 8.5 | 5 |
| Ghana | West Africa | 8.0 | 5 |
| Senegal | West Africa | 6.0 | 4 |
| Cameroon | Central Africa | 5.5 | 4 |
| Zimbabwe | Southern Africa | 3.0 | 3 |

## Scenarios

### baseline
Normal operations reflecting current African cement industry conditions (2024-2025). Parameters calibrated to actual reported industry performance metrics.

### energy_crisis
Energy supply disruptions, fuel price spikes, grid instability. Modelled on real-world events including South Africa load-shedding and Nigeria natural gas supply shortages. Features ~22% higher energy intensity, ~35% higher costs, and reduced capacity utilization.

### green_transition
Accelerated adoption of alternative fuels, energy efficiency upgrades, and carbon reduction initiatives aligned with the GCCA 2050 net-zero roadmap. Features ~28% lower CO2 emissions, 20pp higher alternative fuel substitution, and ~12% improved energy efficiency.

## Features

| Feature | Type | Description |
|---|---|---|
| `plant_id` | string | Unique plant identifier (e.g., NGA-001) |
| `country` | string | Country name |
| `region` | string | African region |
| `scenario` | string | Scenario name |
| `timestamp` | date | Monthly timestamp (YYYY-MM-DD) |
| `year` | int | Year (2020-2025) |
| `month` | int | Month (1-12) |
| `plant_age_years` | float | Age of plant in years |
| `installed_capacity_mt` | float | Installed clinker capacity (million tons/year) |
| `grinding_capacity_mt` | float | Cement grinding capacity (million tons/year) |
| `capacity_utilization` | float | Utilization rate (0-1) |
| `clinker_production_tons` | float | Monthly clinker production (tons) |
| `cement_production_tons` | float | Monthly cement production (tons) |
| `electrical_energy_kwh_per_ton` | float | Electrical energy intensity (kWh/ton cement) |
| `thermal_energy_gj_per_ton` | float | Thermal energy intensity (GJ/ton clinker) |
| `fuel_coal_share` | float | Coal share of thermal fuel mix (0-1) |
| `fuel_gas_share` | float | Natural gas share of thermal fuel mix (0-1) |
| `fuel_petcoke_share` | float | Petroleum coke share of thermal fuel mix (0-1) |
| `fuel_alternative_share` | float | Alternative fuels share (biomass, waste, etc.) (0-1) |
| `co2_kg_per_ton_cement` | float | CO2 emissions intensity (kg CO2/ton cement) |
| `total_co2_tons_monthly` | float | Total monthly CO2 emissions (tons) |
| `maintenance_downtime_hours` | float | Monthly maintenance downtime (hours) |
| `quality_compliance_rate` | float | Quality compliance rate (0-1) |
| `cement_type_opc_share` | float | Ordinary Portland Cement share (0-1) |
| `cement_type_ppc_share` | float | Portland Pozzolana Cement share (0-1) |
| `cement_type_psc_share` | float | Portland Slag Cement share (0-1) |
| `cost_usd_per_ton` | float | Production cost (USD/ton) |
| `export_volumes_tons` | float | Monthly export volumes (tons) |
| `domestic_sales_tons` | float | Monthly domestic sales (tons) |
| `total_revenue_usd` | float | Estimated monthly revenue (USD) |

## Parameter Calibration

All parameters are calibrated against published industry data:

- **Capacity utilization**: Sourced from Dangote Cement annual reports, Global Cement Magazine, and industry capacity surveys. African plants typically operate at 35-75% of nameplate capacity.
- **Energy consumption**: Electrical energy (88-110 kWh/ton) and thermal energy (3.45-3.95 GJ/ton) calibrated against NRCan Cement Benchmark Guide, IEA data, and peer-reviewed studies.
- **CO2 emissions**: 595-670 kg CO2/ton cement, incorporating IPCC process emission factors (0.52 tCO2/t clinker from calcination) plus fuel combustion emissions.
- **Fuel mix**: Calibrated against GCCA Alternative Fuels reports and country-specific energy availability data.
- **Cost per ton**: Derived from Dangote Cement financial reports, LafargeHolcim Africa data, and industry cost benchmarks.

## References

1. USGS Mineral Industry of Africa (2019-2024)
2. Dangote Cement Annual Reports 2024-2025
3. GCCA Cement Industry Net Zero Progress Report 2024/25
4. IEA Cement Energy System Analysis
5. African Development Bank Infrastructure Reports
6. Global Cement Magazine Top 100 2026
7. Natural Resources Canada: Cement Clinker Energy Benchmark Guide
8. MDPI: Alternative Fuels Substitution in Cement Industries (2023)
9. Discover Applied Sciences: Ethiopian GHG Cement Emission Factors (2025)
10. World Cement: Sub-Saharan Africa Industry Reports (2025)

## Usage

```python
import pandas as pd

# Load a specific scenario
df = pd.read_csv("baseline.csv")

# Filter by country
nigeria = df[df["country"] == "Nigeria"]

# Compare scenarios across countries
baseline = pd.read_csv("baseline.csv")
crisis = pd.read_csv("energy_crisis.csv")
green = pd.read_csv("green_transition.csv")

# Example: CO2 emissions by country and scenario
for scenario_name, scenario_df in [("baseline", baseline), ("crisis", crisis), ("green", green)]:
print(f"\n{scenario_name}:")
print(scenario_df.groupby("country")["co2_kg_per_ton_cement"].mean().sort_values())
```

## Reproducing the Dataset

```bash
pip install -r requirements.txt
python generate_dataset.py
python validate_dataset.py
```

## License

CC-BY-4.0