Application of Future Technologies (Machine Learning) for Biofuel (Green Energy) Production

LEVEL: INTERMEDIATE                    HRDF: CLAIMABLE 

Ten (10) CPD Hours approved by MBOT

TRAINERS: 

Dr. Muhammad Ayoub & ASSOCIATE PROFESSOR Dr. Manzoor Ahmed Hashman

 

25 - 26 SEPTEMBER 2024

 

MS TEAMS

 

9.00AM - 5.00PM

 

RM 1,630 FOR PROFESSIONALS

10% Discount for Early Bird (until 25 August 2024) / Group / Students

CONTENT SUMMARY

INTRODUCTION

In the past few years, high consumption of fossil fuels has led to a serious energy crisis due to increased demand of energy because of industrialization and modernization. It resulted in environmental concerns due to greenhouse gas emission with energy security issues. These fossil fuel resources have been getting threshold and exhaust as well as depletion. These issues have prompted the research to find out the alternative energy resources which are technically feasible, environmentally acceptable and economical competitive. Biofuels as an alternative to the petrodiesel are originated by inorganic ingredients instead of fossil fuels. Biodiesel production is abruptly growing worldwide due to its characteristic, such biodegradable, non-toxic, and sustainable energy. Biofuel production cost is a challenging part for its implementation that can be possibly overcome by utilization of waste feedstock as well as costly catalyst replace by available and waste material. The importance of energy systems and its role in economics and politics is not hidden for anyone.

Biodiesel production is not a complex process but time and energy consuming process in which chemical reactions as well as heat and mass transfer mechanisms take place. Many parameters such as the feedstock type, catalyst configuration, solvent agent, temperature, and reaction time can affect the operational performance and the green fuel quality. However, experimental optimization of the process for a certain feedstock is not only time-consuming but also expensive. Simulating the transisterification process based on available experimental data on pilot or industrial scale can support process optimization and contribute to improved plant design. Also, computational tools are effective to determine process limitations and hazardous or undesirable operational conditions

COURSE CONTENT                                           

  • Introduction to Biofuel.
  • Feedstock for Biofuel.
  • Classification of Biofuels.
  • Biomass to Biofuel.
  • Biofuel Future Technologies.
  • Machine Learning for Predictions of Biodiesel Yield
    • Introduction to ML
    • Tools of ML
    • Case Study
  • Hands-on Session


OBJECTIVES

Upon completion of this course, participants will be able to:

  • Introduce Biofuel as alternative and eco-friendly energy source.
  • Utilization of waste biomass into biofuel as well as advance material as catalyst for process
  • Focus on Hydrogen and fuel cell as future technologies for clean, low carbon energy and transport
  • Apply Machine Learning techniques for modeling, and prediction of biodiesel yield


WHO SHOULD ATTEND?

  • University Students
  • Industry People





OUR TRAINERS

1. Dr. Muhammad Ayoub (UTP)

Dr. Muhammad Ayoub is a senior lecturer in Universiti Teknologi PETRONAS. Currently, he is working in the field of Biofuel, Sustainable and Renewable Energy. Clean Environment, De-NOx, Co, Capture and Utilization Catalysis, Biomass conversion using innovative and collaborative research methods. He have more than 100 ISI publications and more than 400 Impact Factor with citation 1880 (H-index 20 and 10-index 37) and number of international conference presentations and papers. He was invited 6 times as plenary/ Keynote speakers in international Conferences. He has been supervised more than 66 undergraduate and postgraduate students and conducting 16 projects to perform research on Biofuel, Co, Solubility & Utilization, Biomass Conversion and Green Catalytic Technology. He is a Project manager of Pak-Mill and chaired the UTP-PAKISTAN International Collaboration Workshop 2018. He received Gold Medal Award of Best Paper Presentation in International Conference of Bio-Sciences 2021, Pakistan.

2. Associate Professor Dr. Manzoor Ahmed Hashmani (UTP)

Associate Professor Dr. Manzoor Ahmed Hashmani is currently working as an Associate Professor in the Universiti Teknologi PETRONAS (UTP), Malaysia. He earned his M.E in 1997 and Ph.D. in 1999 from Nara Advanced Institute of Science & Technology, Nara (Japan). He has authored and co-authored more than 100 research articles published in various journals and conferences of international repute. He has also worked as lead research and development person in a reputable Japanese company for five years. He is a Senior Member of IEEE, executive committee member of IEEE Computational Intelligence Society's, member of Society of Engineering Education Malaysia, member of Malaysia Board of Technology, and member of Pakistan Engineering Council. His current research areas of interest include:- Blockchain Technologies- Artificial Intelligence and Machine Learning- Soft Computing for Optimization

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REGISTRATION FEES

PROFESSIONALS

MYR1,630*

*fee quoted does not include SST, GST, HRDF service fee / VAT or withholding tax (if applicable).

EARLY BIRD/ GROUP/ STUDENT

MYR1,467*

*fee quoted does not include SST, GST, HRDF service fee / VAT or withholding tax (if applicable).

OUR LOCATION

Centre for Advanced & Professional Education (CAPE)

 Level 8, Permata Sapura, Kuala Lumpur City Centre, 50088 Kuala Lumpur​.

CALL US

+605 - 368 7558 /

+605 - 368 8485

DROP US AN EMAIL

cape@utp.edu.my