Al-Khwarizmi Engineering Journal

In this study, copper was removed from an aqueous solution through electromembrane extraction (EME), a new approach that utilises a two-chamber electrochemical cell. It consists of two electrodes (stainless steel cathode and graphite anode) and a solid liquid membrane (SLM). SLM is composed of supporting polypropylene membrane impregnated with1-octanol as an organic solvent and bis(2-ethylhexyl) phosphate (DEHP) as a carrier. The effects of process parameters such as applied voltage, pH and copper concentration on the removal of copper were investigated. Response surface methodology (RSM) was applied to optimise these parameters and their interactions. Results indicated that pH has the major effect on the removal of copper, followed by applied voltage. The squared interaction term in the RSM model has the highest contribution (70.5%), followed by the linear term, thereby confirming the significant interaction among the variables. The optimum conditions include an applied voltage of 60V, pH of 5.18 and initial copper concentration of 5 ppm, which yield to a removal efficiency of 80% after 6 hours of operation. The findings demonstrate the use of electromembrane extraction as an efficient method for the removal of heavy metals and provide valuable insights for future application to other environmental and water treatment processes.

This paper presents a new optimal robust control algorithm based on a proportional-integral (PI) and state feedback controller with a state disturbance observer for the two degrees-of-freedom helicopter system. A disturbance observer is used to improve the robustness of the proposed controller instead of using high gain to reject the external disturbance. Combining the PI controller with the state feedback controller improved the performance of the controlled system. Simulations based on Matlab 2022 are performed to compare the proposed controller with the linear quadratic regulator controller and investigate the performance and robustness of the proposed control method. The comparison between controllers was made under three cases: 1) nominal model, 2) disturbance rejection and 3) system uncertainty. The proposed algorithm shows good performance, which was confirmed clearly by the simulation results that illustrate the transient specifications represented by no overshot, the smallest settling time, and the smallest integral square error. The algorithm also indicates a good choice of objective function based on the infinity norm of the transfer function to ensure high robustness regardless of the external disturbance and parameter variations in the system.

Bipedal robots, which mimic human or animal motion, are expected to perform numerous tasks, such as delivering healthcare, conducting search and rescue missions in dangerous environments and serving industrial applications. Bipedal robots are required to interact with objects or people in their surroundings while performing their planned tasks. The main challenge faced by these robots is their ability to maintain balance in the presence of disturbances, such as external pushing forces applied to them and uneven terrains. Therefore, a push recovery control system that enables these robots to preserve stability while executing their intended tasks must be developed. This study investigates several push recovery control algorithms for bipedal robots operating in static (standing) or dynamic (walking) modes when faced with disturbances. The study further assesses the literature based on three factors: 1) the dynamic model used to represent the robot’s behaviour, 2) the control methods and 3) the required sensors. Moreover, this review paper emphasises the challenges that must be tackled in future research. These issues include the ability of bipedal robots to adapt rapidly to changing conditions in dynamic scenarios, the substantial energy consumption they require and the delays that arise from the complex and nonlinear structure of their movements. Hence, this research suggests some recommendations for effectively tackling these difficulties. 1) Sensory feedback approaches with machine learning algorithms should be employed to develop adaptable balance control systems that quickly learn from and react to different disturbances in real time. 2) Control algorithms that optimally balance stability and energy efficiency, such as predictive control algorithms that emulate the natural reflexes of humans, should be developed. 3) Hierarchical control systems should be used to partition the balance control problem into smaller stages, thus reducing the latency issues related to solving complex nonlinear equations.

For decades, metal corrugated sheets have usually been manufactured using conventional roll-forming machines with lower and upper rollers or a die and a press as the main shaping elements. However, these machines and their related processes present economic disadvantages because of additional expenses required to improve and manage forming tools. To overcome these drawbacks, reconfigurable machines, such as dedicated and flexible manufacturing systems, were used as alternatives; they possess high flexibility for accomplishing forming processes. Reconfigurable machines are designed around a particular family of manufactured outcomes, allowing for high system flexibility. In light of the latest developments in reconfigurable machine design, this study proposes a new sheet metal forming roller called the discrete multi disk roller (MDR) as an alternative to the traditional roller design. Unlike existing processes, the MDR minimises production costs associated with material loss and effectively decreases forming errors. Furthermore, it utilises multi-disk as reconfigurable rollers. The technique and applicable procedure of the MDR are described, and wavy sheets with different dimensions and shapes are formed to verify the applicability of the reconfigurable roller, a critical component in the forming process. Thirteen parts with different configuration profiles were produced using the proposed MDR machine.

This paper introduces an algorithm that enhances open-loop stepper motor motion systems. The objective is to close the loop with fewer hardware requirements, increasing flexibility in speed and weight capacity for storage and retrieval (S/R) machines and reducing cost. The method involves integrating the algorithm into a distributed control system, minimizing hardware, processing power, and maintenance shutdowns. The results highlight the algorithm’s effectiveness. Adjusting torque/speed parameters mitigates motor stalling in S/R machines successfully, leading to improved efficiency. Unlike commercial S/R machines, this revolutionizes closed-loop stepper systems, making them more efficient, relatively cost-effective as well as practical for educational purposes.

This paper presents the basic concept of risk and reviews commonly applied tools and techniques for risk assessment. Generally, risk assessment is a completely experiential decision-making process based on experience and knowledge of risk assistants. This paper emphasises one quantitative/qualitative technique, namely the likelihood/severity matrix approach, which aims to direct the organisation’s attention towards risks that have the highest potential to have a negative effect. This paper’s main contribution lies in introducing a proposed model that utilises the likelihood/severity matrix approach to categorise risks into ‘regions’ and subsequently rank them. This process supports risk managers in making informed decisions to reduce risks effectively. A likelihood/severity matrix was examined through a case study belonging to the electrical and electronic sector to find the critical risks that hinder the assembly line of personal computers. Results showed that the ‘Breaking parts during assembly’, ‘Shocking the components from static electricity discharge’ and ‘Using wrong compatible parts’ risks had the maximum risk score, with values of 10–15 as the most critical risks. These results can influence decision makers in developing actions to mitigate these highlighted risks.

Silicon carbide (SiC) is one of the most promising structural ceramic materials because of its excellent thermal and electromechanical properties. These properties are beneficial for CMC that enhances the performance of composites, especially those containing integrated nanoadditives. In this research, SiC composites were fabricated from SiC at three concentrations, ZnO and Si. The magnetic properties of the mixtures were tested through vibratory sample monitoring. The green samples were sintered in a sintering furnace at 1600 °C in a nitrogen environment. The obtained composites have been tested and characterised using different techniques, including X-ray diffraction, surface morphology using FESEM and network analyser, and the dielectric properties of the samples were tested. On the basis of the sintering environment, silicon nitride was detected in the composite because of the nitriding process along the composite. Weak magnetic and absorbing properties were calculated for all the SiC composites. The dielectric properties of a composite were considered high when the composite tended to be a reflector at a low frequency range and a transmitter as the frequency increased along the frequency band. Therefore, the composite exerted a potential effect on the applications where shielding properties required in microwave range to prevent wave interference.

This study aimed to utilise response surface methodology (RSM) to determine the optimal conditions for extracting urease from a plant source and evaluate its biochemical properties. Chickpea seeds were selected as the source of urease. Results showed that chickpea seeds had a high enzyme activity (3.99 U/mg protein) under the best extraction conditions of pH 7.99, tris base buffer concentration of 0.2, time of 60 minutes and ratio of 1:13 (g: mL). When a second-order polynomial model was used, the predicted correlation coefficient (pred. R2) was 0.8477, the adjusted correlation coefficient (adj. R2) was 0.9333 and the correlation values (R2) were 0.9665, indicating that the predicted models and experimental values agreed very well. The method has the potential to enhance the cost-effectiveness of enzyme production through large-scale manufacturing and offers an economical alternative to other techniques.