Ahmad Khairi Abdul Wahab

University of Malaya, Malaysia, Department of Biomedical Engineering, Faculty of Engineering Building, University of Malaya, 50603 Kuala Lumpur, MALAYSIA, Faculty Member

University of Malaya, Malaysia, Biomedical engineering, Faculty Member

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Ir. Dr. Ahmad Khairi ABDUL WAHAB, Ph.D, MIET, C.Eng. (UK) is currently serving in University of Malaya (UM) as an Associate Professor in Department of Biomedical Engineering, Faculty of Engineering, UM. He started his career in UM from 1999 as a Research Assistant in Department of Chemical Engineering, Faculty of Engineering after graduated from the Cardiff University, Wales, United Kingdom in Electrical Electronics Engineering. He has completed his Master Engineering Science Degree's research (M.Eng.Sc.) in application of neural network as intelligent process controller for a chemical reactor. Then, he continued his research in Ph.D. in Genetic Algorithm, Fuzzy Logic and Neurofuzzy in controlling temperature of a Partially Simulated Exothermic Chemical Reactor in Department of Chemical Engineering and completed his Ph.D. in 2010.

Dr. Ahmad Khairi has started his career as a lecturer since 2006 in Department of Electrical Engineering and currently is one of the faculty members of Faculty of Engineering, UM.
Address: Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.

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Papers by Ahmad Khairi Abdul Wahab

Integrated failure analysis using machine learning predictive system for smart management of medical equipment maintenance

Engineering Applications of Artificial Intelligence, 2023

The study aims to develop the failure analysis predictive models, which prognosticate first fail... more The study aims to develop the failure analysis predictive models, which prognosticate first failure event (FFE), failure-to-year ratio (FYR), and failure rectification group (FRG). The construction of predictive models involved nineteen categories of 13,350 units of medical equipment. We proposed thirteen novel features in assessing medical equipment failures. The failure analysis predictive models were categorised into several classes for training and testing the developed models. There was seven supervised machine learning classifiers and performance metrics applied in the experiment. The experiment demonstrates that Support Vector Machine is the best classifier for the FFE predictive model, which achieves an accuracy of 96.9% after hyperparameter optimisation. Furthermore, Decision Tree is the best classifier for FYR, with an accuracy of 83.9%. Meanwhile, the comparative analysis for FRG discovered that Artificial Neural Network achieved the highest accuracy among others with 76.7% accuracy after the hyperparameter optimisation process. Findings from this study indicate that this failure analysis predictive model functions as a main instrument for conducting predictive maintenance in the direction of smart maintenance practices. Through the developed predictive systems, timely maintenance of medical equipment can be performed. This will also assist healthcare service providers in initiating the remanufacturing and refurbishment programme, ensuring efficient medical care delivery. The suggested framework of machine learning-assisted failure analysis for medical equipment maintenance management may provide clinical engineers with guidance for managing the strategic maintenance management for medical equipment.

Modeling and Experimental Verification of In-House Built Portable Ultrafiltration (PUF) System to Maintain Water Quality

Processes, 2024

At present, over 2.6 billion people live without access to a continuous water supply, and nearly ... more At present, over 2.6 billion people live without access to a continuous water supply, and nearly 900 million people do not obtain drinking water from reliable sources. To solve these problems, one of this study's goals is to come up with a water-supply system that uses a simple, inexpensive, portable ultrafiltration (PUF) unit. To determine the effectiveness of the portable system, water-quality analysis has been carried out to determine if the system produces filtered water from various sources of water, reaching drinking-water standards. A simple model of the system using Darcy's Law was also obtained to predict permeate flux and transmembrane pressure (TMP). Initially, simulation was performed using nominal values taken from the literature for four (4) parameters, i.e., membrane hydraulic resistance, initial rapid fouling constant, mass transfer coefficient, and foulant bulk concentration. By minimizing the error between the model with these nominal values and experimental values, an improved model with updated parameters was obtained using the Evolutionary Programming (EP) approach. With the updated model, the average error between the model and the experiment was reduced from 32% to 9%. This was further validated with new data taken from the experiment. This improved model with the updated parameter was then used to predict the TMP and compared with the experimental value. Contrasting the optimized model with the existing model indicates that the optimized model predicts membrane performance better, leading to a competent and reliable model for the purification of water using a PUF system built in-house.

SVR modelling of mechanomyographic signals predicts neuromuscular stimulation-evoked knee torque in paralyzed quadriceps muscles undergoing knee extension exercise

Computers in Biology and Medicine, 2020

modelling of mechanomyographic signals predicts neuromuscular stimulation-evoked knee torque in p... more

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Reliability of Medical Equipment Through Intelligent Prioritisation Assessment and Predictive Systems with Significant Features

SSRN Electronic Journal

A Strategic Corrective Maintenance Prioritization Assessment for Medical Equipment

6th Kuala Lumpur International Conference on Biomedical Engineering 2021

Simulation of Electrical Properties of Iron Oxide and Titanium Dioxide - Polypyrrole Nanocomposite for Halal Biosensor

2020 IEEE 8th Conference on Systems, Process and Control (ICSPC), 2020

This paper presents the study of electrical properties comparison between Iron oxide-polypyrrole ... more This paper presents the study of electrical properties comparison between Iron oxide-polypyrrole and Titanium dioxide-polypyrrole nanocomposite model. The study was conducted through a simulation and calculation approach. The results showed that titanium dioxide had a better electrical resistivity and faster time response in comparison to Iron oxide. The Titanium dioxide-polypyrrole nanocomposite showed the good potential to be adopted for the development of a fast response Molecularly Imprinted Polymer (MIP) based DNA biosensor for halal detection.

Foot Orthosis Restrained Ankle Movements do not Influence Pedal Power Production during Recumbent Cycling

Design and performance studies of enhanced fuzzy logic controller for a partially simulated exothermic chemical reactor

Design and Control of an Experimental Partially Simulated Exothermic Reactor System

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Frontiers in Public Health, 2021

The advancement of technology in medical equipment has significantly improved healthcare services... more The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel fea...

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Development of biohydrogen production by photobiological, fermentation and electrochemical processes: A review

Renewable and Sustainable Energy Reviews, 2014

Production of biohydrogen has the potential to be a renewable alternative to current technologies... more Production of biohydrogen has the potential to be a renewable alternative to current technologies. There are varieties of technologies for biological hydrogen production mechanisms including biophotolysis, photo fermentation, dark fermentation and hybrid biohydrogen production by electrochemical processes. In these studies, a review on the recent developments of biohydrogen production is presented. First, the theoretical principles of biophotolysis by cyanobacteria and green micro algae, as well as direct and indirect of biophotolysis process on hydrogen production are described. Secondly, practical aspects and fundamental of biological hydrogen production processes by photo and dark fermentation are reviewed. This work also involved comparison of the maximum H 2 yield, bacterial strains, operating condition, suitable substrates, and mathematical models for fermentative hydrogen production. A new hybrid biological hydrogen production processes by using the electrochemical process is then proposed. This study can also be used to improve the basic and current knowledge about the performance of the biophotolysis, fermentative and electrochemical process in producing hydrogen gas as the alternate fuel.

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Control of a partially simulated exothermic chemical reactor using fuzzy logic based methods

Optimal Production of Biohydrogen Gas via Microbial Electrolysis Cells (MEC) in a Controlled Batch Reactor System

Chemical Engineering Transactions, 2013

A Microbial Electrolysis Cells (MECs) is a slightly modified microbial fuel cells (MFCs) where a ... more A Microbial Electrolysis Cells (MECs) is a slightly modified microbial fuel cells (MFCs) where a small amount of electricity is applied to the anode chamber to suppress the production of methane. Oxygen is kept out of the cathode chamber to assist bacterial oxidation of organic matter present in the wastewater to produce hydrogen, a gas which is the becoming the most attractive energy source. While MECs has tremendous potential, the development of this technique is still in its infancy. The goal of this work is to optimize the production of biohydrogen gas by selecting the optimum current and controlling applied voltage in MECs using batch reactor. The mathematical model of the MECs is based on material balances with the integration of bio-electrochemical reactions describing the effect of applied voltage on the performance of MECs batch reactor. The behaviour of the system differs significantly as the value of applied voltage is changed and gives a significant influence on the hydr...

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Neural network inverse model based control strategies to control a tank-level system

Viscosity of Saraline-based super lightweight completion fluid in high pressure-temperature conditions

Development of partially simulated chemical reactor for the implementation of advanced control systems

In this work, a partially simulated exothermic chemical reactor (PARS-EX) pilot plant available i... more In this work, a partially simulated exothermic chemical reactor (PARS-EX) pilot plant available in our department is designed and utilized to implement and test various conventional and advanced control strategies. In this reactor, an exothermic chemical reaction is simulated, where heat energy generated from the reaction is replaced by a calculated and controlled steam flow rate through a coil immersed in the reactor. A control valve is used to regulate flow rate of fresh water into a heat exchanger. The heat exchanger then cools the process fluid and recycles it back to the reactor, hence controls the temperature of the reactor. Prior to the online implementation, several advanced controllers are simulated for tracking required temperature of the reactor. The online advanced control results are made possible due to the developed customized graphical user and process interface. The work also highlights the software development involved. The software is designed so that it is capabl...

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Estimation of Electrically-Evoked Knee Joint Torque from Mechanomyography Using Support Vector Regression Methodology

Comparison of PID and neural network model-based controller in a fed-batch microbial electrolysis cell reactor for optimal bio-hydrogen gas production

Hydrogen production processes from microbial electrolysis cell (MEC) reactors are very nonlinear ... more Hydrogen production processes from microbial electrolysis cell (MEC) reactors are very nonlinear and highly complex due to the presence of microbial interactions and highly complex phenomena in the system. Its complexity makes MEC system difficult to operate and control under optimal conditions. This studies focus on the comparative controller performance in a feedback control system; PID and neural network (NN)-based controllers. The comparative study has been carried out under optimal condition for the production of bio-hydrogen gas wherein the controller output are based on the correlation of optimal current and voltage to the MEC. Various simulation tests involving multiple set-point changes and disturbances rejection have been evaluated and the performances of both controllers are discussed. A Ziegler-Nichols tuning method has been used to design the PID controller, while the neural network controller has been designed from the inverse response of the forward MEC model. The neu...

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Fuzzy logic controller implementation on a microbial electrolysis cell for biohydrogen production and Storage

Chinese Journal of Chemical Engineering, 2021

Abstract This work presents the implementation of Fuzzy Logic Control (FLC) on a Microbial Electr... more Abstract This work presents the implementation of Fuzzy Logic Control (FLC) on a Microbial Electrolysis Cell (MEC). Hydrogen has been touted as a potential alternative source of energy to the depleting fossil fuels. MEC is one of the most extensively studied method of hydrogen production. The utilization of biowaste as its substrate by MEC promotes the waste to energy initiative. The hydrogen production within the MEC system, which involves microbial interaction contributes to the system’s nonlinearity. Taking into account of the high complexity of MEC system, a precise process control system is required to ensure a well-controlled biohydrogen production flow rate and storage application inside a tank. Proportional-Derivative-Integral (PID) controller has been one of the pioneer control loop mechanism. However, it lacks the capability to adapt properly in the presence of disturbance. An advanced process control mechanism such as the FLC has proven to be a better solution to be implemented on a nonlinear system due to its similarity in human-natured thinking. The performance of the FLC has been evaluated based on its implementation on the MEC system through various control schemes progressively. Similar evaluations include the performance of Proportional-Integral (PI) and PID controller for comparison purposes. The tracking capability of FLC is also accessed against another advanced controller that is the model predictive controller (MPC). One of the key findings in this work is that the FLC resulted in a desirable hydrogen output via MEC over the PI and PID controller in terms of shorter settling time and lesser overshoot.

A Systematic Review of Medical Equipment Reliability Assessment in Improving the Quality of Healthcare Services

Frontiers in Public Health, 2021

Medical equipment highly contributes to the effectiveness of healthcare services quality. General... more Medical equipment highly contributes to the effectiveness of healthcare services quality. Generally, healthcare institutions experience malfunctioning and unavailability of medical equipment that affects the healthcare services delivery to the public. The problems are frequently due to a deficiency in managing and maintaining the medical equipment condition by the responsible party. The assessment of the medical equipment condition is an important activity during the maintenance and management of the equipment life cycle to increase availability, performance, and safety. The study aimed to perform a systematic review in extracting and categorising the input parameters applied in assessing the medical equipment condition. A systematic searching was undertaken in several databases, including Web of Science, Scopus, PubMed, Science Direct, IEEE Xplore, Emerald, Springer, Medline, and Dimensions, from 2000 to 2020. The searching processes were conducted in January 2020. A total of 16 ar...

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