Process Performance for Decarbonization at PT. Cemindo Gemilang, Tbk

Energy efficiency and digitalization are core to PT. Cemindo Gemilang sustainability strategy. To improve energy efficiency, the company has adopted ABB Industrial Software for controlling, stabilizing, and optimizing operations of Mills and Kilns at its Bayah Integrated Plant. Focus is on Raw Mill, Coal Mill, Cement Mill, Calciner, Kiln and Cooler for both Kiln lines. Benefits of 9 -12 % increase in production and 2 - 8 % decrease in specific power consumption in Cement mill have been realized. While in Coal milling there has been a decreased specific power consumption of 3 – 6 %.

This first appeared in the December 2023 International Cement Review publication.

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PT. Cemindo Gemilang was established in 2011. It has Semen Merah Putih brand in Indonesia and Chinfon Cement brand in Vietnam.  

Currently company has 6 (six) plants across Indonesia at Bayah, Ciwandan, Gresik, Medan, Bengkulu, Pontianak, operates 3 (three) cement grinding plants in Muara Jawa (East Kalimantan), Batam and Cibitung with a joint operational scheme (KSO) and 2 (two) plants across Vietnam in Trang Kenh and Hiep Phuoc. The cement production capacity of all these plants in 2022 reached 15.1 million tons per annum. 

Bayah Integrated Cement Plant started production in December 2015, and then added a second clinker production line in 2020, making clinker production capacity of 6.4 million tons per annum. 

Main equipment at Bayah cement plant are as below:

Section

Line #1

Line #2

Raw Mill

2 × Gebr. Pfeiffer MPS 5300 B

2 × FLSmidth OK 39-4

Coal Mill

Gebr. Pfeiffer MPS 4500 BK

FLSmidth ATOX 45/35

Kiln & Preheater

2 string ILC-5 Stage PH, 10000 TPD clinker, WHRS (15 MW)

2 string ILC-5 Stage PH, 10000 TPD clinker, WHRS (15 MW)

Cooler

IKN

FLSmidth Cross-bar 24×67

Cement Mill

2 × Gebr. Pfeiffer MPS 5300 BC

 

Sustainability Strategy
Expert Optimizer focus
Site Customizations
Benefits
Development

Sustainability Strategy of PT. Cemindo Gemilang Tbk

PT Cemindo Gemilang Sustainability Strategy has been defined in 4 (four) pillars, namely:

  1. Climate protection
  2. Efficient use of resources
  3. Health, safety, and the environment
  4. Grow with the community

Energy efficiency and digitalization is core to PT. Cemindo Gemilang’s sustainability strategy for climate protection by reducing Greenhouse Gas emissions. To improve energy efficiency, the company has adopted several ABB digital technologies.

  • Loop Performance Monitoring – ensuring stable and properly tuned base level PIDs for process control.
  • Inferential Modelling Platform (IMP) – leveraging machine learning to develop inferentials for hard to measure or unreliable properties on the plant, such as Blaine and faulty analysers.
  • Ability™ Expert optimizer, ABB’s advanced process control solution for controlling, stabilizing and optimizing operations.

Expert Optimizer is intended for multi-variable control and optimization of industrial processes. Due to its combination of model predictive control, fuzzy logic and inferential AI / ML technologies, the software enables the plant to automatically make the best operational decisions accurately and consistently.

Reducing carbon emissions will be targeted using Expert Optimizer system by:

  1. Increasing usage of alternative fuels
  2. Reduction of thermal energy consumption
  3. Reduction of electrical energy consumption

ABB Ability™ Expert Optimizer focus

Specific Site Customizations

Kiln

there were some operational challenges in the Kiln coal feeder, frequent coal pulsation was observed that disturbs the stability of the pyro process by creating CO spikes in kiln inlet and temperature increase in calciner exit. With the Expert Optimizer system, a temporary solution was given to minimise the coal feeder issue until permanently resolved. Rate of increase in coal feeder blower outlet pressure (due to high amount of coal flow) was taken as input for reducing the Calciner fuel to control Calciner exit temperature.

Coal mill

was operated continuously due to safety reasons while using the high volatile coal. A strategy was made in Expert Optimizer for three different production levels where the operator can select a different capacity of operation to save specific power as production rate is not a prime requirement.

Estimated Benefits and Actual Realization

Commissioning of Expert Optimizer started in November 2022. Commissioning including performance tests are completed for coal mills and cement mills. The remaining sections are expected to be completed by end of August 2023.

Performance tests were conducted during the period April to May 2023. During the test it was observed that there is a reduction in standard deviation of up to 60% for key process parameters such as Mill DP, Mill KW, Mill Exit Temperature, Mill Draft, Mill Fan KW.

Summary of achieved benefits:

Section

Performance Parameter

Potential Improvement (%)

Actual Test Result

Coal Mill #1

Elect.Energy

3 to 4% ↓

6.6% ↓

Coal Mill #2

Elect.Energy

3 to 4% ↓

3.5% ↓

Cement Mill #1

Productivity

2.5 to 3 %↑

9.6% ↑

 

Elect.Energy

2 to 2.5 ↓

2.5% ↓

Cement Mill #2

Productivity

2.5 to 3 %↑

12.2% ↑

 

Elect.Energy

2 to 2.5 ↓

8.5% ↓

 

The comparison of mill operation with and without Expert Optimizer shown below illustrates improved stability of the operation:3




In order to ensure effective utilization of Expert Optimizer, it is monitored during plant daily operation review meeting and monthly presented to the Management. Logbook is maintained to note down stoppage reasons that are analysed by Expert Optimizer champion and discussed with ABB on a regular basis.

With the adoption of Expert Optimizer, PT. Cemindo Gemilang is expecting to reduce its CO2 emission by approximately 10 ‒ 12 Kg CO2/t cement equivalent. 

Development of Inferentials

Online inferentials, otherwise known as softsensors, are being developed using ABB’s Inferential Modelling Platform for prediction of quality parameters e.g. clinker free lime based on the historical data.

Inferential measurement is an increasingly used methodology involving machine learning that allows online estimation of target variables. These estimates can be then integrated in an Advanced Process Control application (such as Expert Optimizer) for increased accuracy and frequency of process states.

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