02. Master's Thesis
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Item Embargo COMPUTATIONAL FLUID DYNAMICS IN PANCREATICOBILIARY JUNCTION(Nazarbayev University School of Engineering and Digital Sciences, 2024-04) Turgaliyev, AlimPancreaticobiliary maljunction without biliary dilation is associated with pancreaticobiliary reflux, a pathophysiologic factor underlying a wide range of diseases, including gallbladder cancer. This work used computational flow simulations to examine the mechanisms and relevant geometric features that influence pancreaticobiliary reflux and assess the impact of surgical interventions. The results suggest that the refilling phase is the primary mechanism driving pancreaticobiliary reflux. Moreover, the cystic duct diameter was the most critical factor determining the reflux dynamics. Furthermore, the configuration of the baffle system (the baffle height ratio and the number of baffles) affected the dynamics of pancreaticobiliary reflux. Also, the study underscores the potential therapeutic efficacy of cholecystectomy and endoscopic retrograde cholangiopancreatography in managing pancreaticobiliary reflux in cases of pancreaticobiliary maljunction without biliary dilatation. These interventions offer promising avenues for reducing reflux-related complications and mitigating the progression of associated diseases.Item Open Access TOWARDS MORE RELIABLE DRUG TOXICITY PREDICTION: AN ENSEMBLE APPROACH(Nazarbayev University School of Engineering and Digital Sciences, 2024) Yarovenko, VladislavThe development of a single pharmaceutical drug is a time- and resource-consuming process with a high likelihood of rejection. In recent years, the cost-effectiveness of a single drug has decreased drastically, as the criteria for passing has become more rigorous. A huge fraction of attrition rates is caused by the toxicity of chemical compounds. Recent findings in Machine Learning (ML) have revolutionized the drug toxicity prediction field, developing many model architectures and data representations. The faced challenges are different ways of representing the molecules’ chemical structure, as well as many different toxicity types. This study proposes a novel drug toxicity prediction framework. It uses several classification models, based on different data representations and different ways of combining their features. The evaluation of six different datasets with different toxicity types shows that choosing majority voting across all models can improve the ROC AUC score and accuracy. Using a single classification model to combine these datasets demonstrates that it is possible to achieve 84% accuracy on data with various toxicity types. The findings of this research provide insights into the application of ML in pharmaceutical research. Improving current methods of toxicity assessment can have a positive effect on the efficiency and cost-effectiveness of drug development.Item Restricted ENHANCING EMERGENCY RESPONSE: THE ROLE OF INTEGRATED VISION-LANGUAGE MODELS IN IN-HOME HEALTHCARE AND EFFICIENT MULTIMEDIA RETRIEVAL(Nazarbayev University School of Engineering and Digital Sciences, 2024-06) Abdrakhmanov, RakhatIncidents of in-home injuries and sudden critical health conditions are relatively common and necessitate swift medical expertise. This study introduces an innovative use of vision-language models (VLMs) to elevate human healthcare through improved emergency recognition and efficient multimedia search capabilities. By harnessing the combined strengths of large language models (LLMs) and vision transformers (ViTs), this study enhances the analysis of both visual and textual information. We propose a framework that utilizes the PrismerZ VLM in both its Base and Large forms, along with a key frame selection (KFS) algorithm, to pinpoint and exam- ine pertinent images within video streams. This allows for the creation of enriched datasets, filled with images that are paired with descriptive narratives and insights gained from visual question answering (VQA). Through the integration of the CLIP- ViT-L-14 model and the MongoDB Atlas cloud database, we developed a multimodal retrieval system that achieves complex query handling and improved user experience. Additionally, this research undertakes data collection to assess the system’s adaptabil- ity, providing proof of concept and refining the framework. The results showcase the system’s robustness, evidenced by high accuracy rates—86.5% in image captioning and 92.5% in VQA tasks—on the kinetics dataset. When tested with human subject data, the PrismerZ Large model achieved 85.8% accuracy in image captioning and 87.5% in VQA tasks. This performance was further enhanced through fine-tuning with the GPT-4 based Chat GPT, one of the largest language assistants, leading to a 20% improvement in semantic text similarity as measured by the BERT model. The PrismerZ models also stand out for their speed, with the Base and Large versions processing image captioning and VQA tasks in just seconds, even on the NVidia Jet- son Orin NX edge device. These findings confirm the system’s reliability in real-life scenarios. The multimodal retrieval system achieved top performance with a mean average precision at k (MAP@k) of 93% and mean reciprocal rank (MRR) of 94.79% on the kinetics dataset, maintaining an average search latency of merely 0.33 seconds for text queries. This research significantly propels the fields of human activity recognition (HAR) and emergency detection forward, carving out new paths for anomaly detection and enriched multimedia understanding. Our objective in integrating the VLM with multimedia information retrieval is to establish new benchmarks for hu- man care, improving its timeliness, comprehensiveness, and efficiency in accessing multimedia dataItem Restricted PREDICTING SOLAR FLARE OCCURRENCES USING TIME-SERIES DATA FROM NASA’S API FOR SPACE WEATHER FORECASTING(Nazarbayev University School of Engineering and Digital Sciences, 2024) Seit, DamirThis thesis investigates the complicated area of solar flare and Coronal Mass Ejection (CME) prediction using data science approaches and NASA’s Application Programming Interface (API) for the purpose of comparing time-series prediction models in terms of accuracy and interpretability. Quick, intense solar eruptions labeled as CMEs and solar flares have significant impacts on space weather and Earth’s technological systems. The data for this research is taken from the open NASA API service. To understand the underlying relationships governing these solar events, we utilize both state-of-the-art LSTM and well-known models such as Autoregressive Integrated Moving Average (ARIMA), Linear Regression, and Autoregressive (AR) model. The dataset was gathered using a specifically developed Python application with the help of RESTful API. A key part of our research is the dual assessment of these models, which evaluates in terms of both forecast accuracy and interpretability. The interpretability of the tested models is measured using the SOC (simulatability operations count) score metric, which counts the number of arithmetic operations performed to predict a single test case. This study shows that simpler approaches in univariate time-series forecasting perform better than more complex ones in terms of accuracy and interpretability with small dataset. However, only Linear Regression made a reasonable predictions of extreme peak events.Item Restricted ASSESSMENT OF MICROPLASMA SPRAYING OF BIOACTIVE COATINGS ON TITANIUM IMPLANTS FOR BIOCOMPATIBILITY IN VITRO(Nazarbayev University School of Engineering and Digital Sciences, 2024-04) Nwachukwu, China JesseThis study presents an assessment of microplasma spraying (MPS) as a technique for applying bioactive coatings on titanium implants to enhance biocompatibility in vitro. The research aims to evaluate the effectiveness of MPS in promoting osteogenic differentiation and antimicrobial activity of titanium implants through in vitro assays. Surface-modified titanium implants with zirconium, hydroxyapatite, tantalum, and titanium coatings of varying roughness were fabricated using MPS. Osteogenic differentiation potential was assessed via alkaline phosphatase (ALP) activity and alizarin red staining, revealing significant variations in mineralization among the different coatings. Antimicrobial studies utilizing Escherichia coli as a model organism demonstrated differential growth kinetics in the presence of implant material extracts, suggesting potential antimicrobial properties associated with specific coatings. The results underscore the importance of surface modifications in influencing osteogenic differentiation and antimicrobial activity of titanium implants. Zirconium, hydroxyapatite, and tantalum coatings exhibited distinct mineralization patterns and antimicrobial effects compared to uncoated titanium, highlighting the potential of MPS in enhancing the biocompatibility of titanium implants. These findings contribute to the understanding of surface engineering techniques for implant materials and pave the way for further research aimed at optimizing coating formulations and deposition parameters to improve implant performance and clinical outcomesItem Restricted PHOTOTOUCH: SOFTWARE AND HARDWARE METHODS FOR PHOTOELASTIC TACTILE SENSOR(Nazarbayev University School of Engineering and Digital Sciences, 2024-04) Zhuzbay, NurdauletThis thesis introduces a novel approach for predicting force at the fingertips of tendon driven robotic fingers using the photoelastic effect. The core of this technique lies in the detection of stress-induced birefringence in silicone, which becomes visible as distinctive fringe patterns under polarized light. These patterns emerge when tendons, embedded within a silicone matrix, apply forces that compress the material. They are not merely visual markers, but also contain valuable data about the applied forces and their distribution within the silicone. To extract and utilize this data effectively, Convolutional Neural Network (CNN) was employed, specially designed to analyze and interpret the intricate fringe patterns. Thousands of these images were captured in various force application states, resulting in a substantial dataset for the CNN to learn from. The finger was used to show the position control and force control capabilities of photoealastic tactile sensor. It was successful in following the sine wave during force control mode, with RMSE of 0.59 N at a frequency of 0.05 Hz. This document will further delve into relevant literature, elaborate on the research methodology, describe the experimental setup, and present preliminary findings. Col lectively, these components unveil the immense potential of the proposed system in augmenting the tactile capabilities of robotic appendages, with far-reaching implica tions for the fields of robotics and automated systems.Item Restricted FIBER OPTIC SHAPE SENSORS FOR DEVICES(Nazarbayev University School of Engineering and Digital Sciences, 2024-04) Karzhaubayeva, DanaThe usage of fiber optics for sensing purposes become popular in recent years due to its advantages such as compatibility, flexibility, and high sensitivity. The literature review observed different types of fiber optics sensors, their applications, and strengths and weaknesses. The setup, which provides real-time shape and temperature sensing, was constructed during this study. The purpose of this work is to conduct laboratory experiments to design a setup that will sense strain and temperature values by fiber optical fibers in real time. Insertion into phantom results illustrate how the shape of the inserted needle changes through different layers of it. Radiofrequency thermal ablation experiment results demonstrate how the temperature was sensed by fibers based on their positions in a heated phantom.Item Open Access DEVELOPMENT OF INTERFEROMETRIC OPTICAL FIBER BIOSENSOR FOR HCC1806 BREAST CANCER CELL DETECTION(Nazarbayev University School of Engineering and Digital Sciences, 2024-04) Meirambekuly, TimurThis thesis explores the use of semi-distributed interferometric (SDI) optical fiber biosensors for detecting breast cancer cells, with a focus on assessing the potential and limitations of this technology. Given the critical importance of early detection in improving breast cancer outcomes, this research aims to advance diagnostic methodologies by leveraging the sensitivity and specificity advantages of optical fiber technology, combined with targeted antibody functionalization. The study commenced with the fabrication of optical fiber sensors, incorporating interferometric tips to single-mode optic fiber pigtails. Calibration was performed using specialized software and equipment, selecting sensors with optimal sensitivity based on their performance in standardized tests. These sensors were then functionalized with CD44 antibodies, targeting the detection of HCC1806 breast cancer cells across a range of concentrations. Experimental trials were conducted to evaluate the sensors' ability to detect cell concentrations from sterilized PBS up to 10^6 cells per ml, with data collection at regular intervals.Item Embargo PREPARATION OF LIFEPO4 -BASED ELECTRODES WITH MAGNETO-SENSITIVE IRON OXIDE (FE2O3 ) NANOPARTICLES UNDER MAGNETIC FIELD(Nazarbayev University School of Engineering and Digital Sciences, 2024) Emmanuel, Iheonu NdukaLithium iron phosphate (LiFePO4) is one of the most widely utilized cathode materials in lithium-ion batteries (LIBs) due to its low cost and environmental safety. Despite this attribute, they still face significant challenges, including high rates and prolonged cycling. However, research has shown that utilizing a magnetic field (MF) can help tackle these issues, thereby improving the ion conductivity and inhibiting polarization of the LiFePO4 cathode. In this study, LiFePO4 cathodes were produced by subjecting them to MF (LFP-MF), while others were optimized using magnetic-sensitive Fe2O3 nanoparticle additives in two concentrations (LFP+1 wt% Fe2O3-MF and LFP+3 wt% Fe2O3-MF). The cathodes were compared to conventional LiFePO4 electrodes prepared under the same conditions but without applying MF (LFP-WMF, LFP+1% Fe2O3-WMF, and LFP+3% Fe2O3-WMF). Application of MF improved the materials' electrochemical properties, contributing to the battery's superior electrochemical performance. The lithium-ion diffusion coefficients of LFP-MF (4.1376 × 10-4 cm2 S-1), LFP+1% Fe2O3-MF (4.0614 × 10-4 cm2 S-1), and LFP+3% Fe2O3-MF (2.3021 × 10-4 cm2 S-1) were more significant than those of LiFePO4 cathodes without subjecting to the magnetic field. Furthermore, the cathodes subjected to MF had higher reversible capacity and a reduced capacity decay than those without MF at an enhanced rate capability greater than 1 C. This study discovered that an MF improves the high-rate performance of LiFePO4 cathodes.Item Restricted IMPROVING THE SPECIFICITY AND SENSITIVITY OF OPTICAL SENSORS FOR BIOLOGICAL PURPOSES(Nazarbayev University School of Engineering and Digital Sciences, 2024-04) Adilzhankyzy, AinaThe development of antibody-detecting biosensors using interferometric devices is a promising area in biomedical engineering. In this study, a semi-distributed interferometer (SDI) was employed as the biosensor device, utilizing a simplified Fabry-Perot structure. The sensor was functionalized using a combination of surface functionalization and silanization, with nanostructuring to enhance the surface area through the application of gold nanorods. The focus of the research was on the detection of two key biological analytes: the CD44 cancer biomarker and the A33 glycoprotein of the vaccinia poxvirus. The fabrication and functionalization processes were optimized for sensitivity and specificity, with the aim of achieving real-time, label-free detection of these important biomarkersItem Restricted CLASSIFICATION OF BABY CRIES INTO DISTINCT CATEGORIES USING CONVOLUTIONAL NEURAL NETWORKS(CNN) WITH SOUND AND SPECTROGRAM ANALYSIS(Nazarbayev University School of Engineering and Digital Sciences, 2024-03) Tulegenov, MaxatThe act of a baby crying is a complex form of communication that reflects various physical, medical, and emotional states. Understanding the nuances within baby cries is essential, as it provides valuable insights into the baby’s needs and can assist in the early detection of developmental disorders and medical conditions. Machine Learning (ML) and Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), coupled with sound processing and data augmentation, play a pivotal role in this endeavor. This research explores methods encompassing data preprocessing, feature extraction, postprocessing, and classification. A primary focus is acoustic analysis and CNN for automatic feature extraction.Item Restricted BENCHMARKING LARGE LANGUAGE MODELS IN KAZAKH LANGUAGE(Nazarbayev University School of Engineering and Digital Sciences, 2024-04) Maxutov, AkylbekItem Open Access ADVANCED CIRCUIT CONFIGURATIONS FOR RF WIRELESS POWER TRANSFER(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-26) Kudaibergenova, ZhanelTechnology for wireless power transfer (WPT) has gained more importance in the contemporary world. The upsurge spike can be a result of the WPT system's ability to power devices without the use of traditional connections. In particular, near-field WPTs have a wide range of applications, including wireless sensors, IoT, biomedical implants, RFID, and consumer electronics. It is essential to emphasize that the WPT system can be realized in a number of ways, one of which is a defected ground structure technique. This approach is well-established for its simple design process and compact system. Despite this recently developed DGS-based WPTs demonstrate poor performance, in other words, low power transfer efficiency in practical validations. The inevitable factors, such as imperfections of lumped elements, the in-house fabrication, and energy losses during transfer, have an impact on the experimental results. Therefore, various performance enhancement strategies have to be considered to realize the compact and efficient WPT system. In this regard, one of the promising methods for improving WPT operation is the use of metamaterial, which is an artificial material with unique electromagnetic features. As a result, this thesis work focuses on the development of compact and efficient WPTs applicable to various fields and on performance enhancement strategies based on metamaterial utilizationItem Restricted VISION-LANGUAGE MODELS ON THE EDGE: AN ASSISTIVE TECHNOLOGY FOR THE VISUALLY IMPAIRED(Nazarbayev University School of Engineering and Digital Sciences, 2024-06) Arystanbekov, BatyrVision-Language Models, or VLMs, are deep learning models at the intersection of Computer Vision and Natural Language Processing. They effectively combine image understanding and language generation capabilities and are widely used in various as sistive tasks today. Nevertheless, the application of VLMs to assist visually impaired and blind people remains an underexplored area in the field. Existing approaches to developing assistive technology for the visually impaired have a substantial limitation: the computation is usually performed on the cloud, which makes the systems heavily dependent on an internet connection and the state of the remote server. This makes the systems unreliable, which limits their practical usage in everyday tasks. In our work, to address the issues of the previous approaches, we propose utilizing VLMs on embedded systems, ensuring real-time efficiency and autonomy of the assistive module. We present an end-to-end workflow for developing the system, extensively covering hardware and software architecture and integration with speech recogni tion and text-to-speech technologies. The developed system possesses comprehensive scene interpretation and user navigation capabilities necessary for visually impaired individuals to enhance their day-to-day activities. Moreover, we confirm the prac tical application of the wearable assistive module by conducting experiments with actual human participants and provide subjective as well as objective results from the system’s assessment.Item Open Access MACHINE LEARNING-DRIVEN PREDICTION OF FLUORESCENT PROBE PROPERTIES: BRIDGING THE GAP BETWEEN PREDICTION AND EXPERIMENTATION(Nazarbayev University School of Engineering and Digital Sciences, 2024-04) Bolatbek, AisanaThe development of organic fluorescent materials needs quick and precise predictions of photophysical characteristics for techniques like high-throughput virtual screen ing. However, there is a challenge caused by the constraints of quantum mechanical computations, experiments, and time. This thesis investigates the field of machine learning-assisted fluorescence probe design to answer this difficulty. The main part of this investigation is the utilization of a substantial database of optical properties of organic compounds that was collected from various scientific papers. One of the complicating factors of this database is the presence of missing data which stems from the collection from various sources, and this inconsistency is examined with the use of a range of imputation methods. Furthermore, the thesis aims to construct predic tive models that can forecast properties that are inherent to fluorescent compounds such as quantum yield, absorption and emission spectra, among others. This research aims to pave the way for a more efficient and targeted approach to fluorescent probe design.Item Open Access FORMAL MODEL DEFINITION OF WEB SERVICE BEHAVIOR FROM SOURCE CODE IN REWRITE LOGIC(Nazarbayev University School of Engineering and Digital Sciences, 2024) Zhangeldinov, OlzhasDevelopers of web applications strive for implementing state-of-the-art design patterns. One of them is microservice architecture design, which increases the number of web services employed in the applications’ back end. Formal verification may help to verify the safe and proper interaction between concurrent web services. Most of the current tools focus on verification of existing formal models defined using specification languages such as BPEN (Business Process Execution Language), WS-CDL (Web Service Choreography Description Language), and recently, Conductor. We propose a framework for building formal models of web service architectures using an imperative programming language called WAFL - Web Architecture Formal Language. We also provide a way to define temporal logic properties based on assertions defined using WAFL. The implementation of the framework was realized using the Maude rewrite logic language as an extension to the language itself. The advantage of such a framework is that it provides a way for software developers to model web service architectures without knowledge of formal modelling languages and with little understanding of formal verification.Item Restricted DATA AUGMENTATION AND TRANSFER LEARNING IN DETECTION OF COVID-19(Nazarbayev University School of Engineering and Digital Sciences, 2024-06) Aimenov, AltairGlobal healthcare systems are facing unprecedented challenges as a result of the COVID-19 pandemic, which calls for quick and precise diagnostic instruments to stop its spread. In this study, we investigate the application of transfer learning in convolutional neural networks (CNNs) and the influence of data augmentation in de- tecting COVID-19 from chest X-ray images. We leverage a dataset comprising images from patients diagnosed with COVID-19 and images corresponding to non-COVID cases, and as part of a transfer learning approach, we utilize four well known CNN models: ResNet50, MobileNet V2, EfficientNet V2, and MobileNet V3. A key focus of our research is the systematic investigation of data augmentation factors and their impact on model performance. Through varying the intensity and types of data aug- mentations, such as rotations, flipping and zooming, we seek to optimize the models’ ability to generalize from training data to real-world scenarios. Our findings reveal that precise calibration of data augmentation significantly en hances the diagnostic capabilities of the models. While increased augmentation gen erally improves sensitivity and specificity, excessive augmentation diminishes mod- els’ performances due to overfitting on non-realistic features. As another result, MobileNet V2 and MobileNet V3 show the highest specificity scores of 0.72 and 0.70, while EfficientNet V2 demonstrates superior sensitivity of 0.96, indicating the strengths and trade-offs of different architectures. These results, assessed through a comprehensive set of metrics including accuracy, sensitivity, specificity, precision, recall, F1 score, and AUC-ROC, underscore the effectiveness of deep learning meth- ods in COVID-19 identification and the crucial role of tailored data augmentation in improving model robustness. The implications of our results extend to clinical prac- tice and public health, highlighting the potential of integrating advanced machine learning technologies into healthcare workflows to enhance diagnostic efficiency and patient care. Looking ahead, we propose further exploration into additional imaging modalities, the integration of multi-modal data, and more sophisticated data aug- mentation techniques, such as usage of Generative Adversarial Networks (GANs), to refine diagnostic accuracy. Overall, our study reinforces the significance of transfer learning and deep learning in addressing the urgent challenges posed by infectious diseases like COVID-19, paving the way for more sophisticated diagnostic tools.Item Restricted DETECTION OF BIOMARKERS IN TEARS WITH FIBER OPTIC BIOSENSORS(Nazarbayev University School of Engineering and Digital Sciences, 2024-04) Seipetdenova, SabiraTears play an important role in maintaining the normal functioning of the eyes. They protect the eye from the harmful effects of the environment and provide hydration. Despite the fact that tears mostly consist of water, their proteomic composition is very diverse and can vary depending on the emotional and physical condition of a person. In addition, collecting tears is not difficult and does not require invasive intervention. This fact makes tears an attractive material for research. Scientists are widely working on the study of the proteomic composition of tears and the discovery of new biomarkers of various diseases in them. The detection of biomarkers in tears has every chance of becoming a new branch of disease diagnosis. At the moment, a large number of biomarkers have already been found in tears and methods for their diagnosis are being actively developed. Biomarkers of diabetic retinopathy are among the most well-studied biomarkers in tears. LCN1 and VEGF are the most well-known representatives of biomarkers of others. In a normal state, the LCN1 protein is responsible for neutralizing harmful lipid molecules, but in pathological conditions, it causes prolonged inflammation. In turn, at normal concentrations, VEGF is responsible for the moderate development of new blood vessels, but at high concentrations, it causes pathological neovascularization. Determining changes in the concentrations of these proteins using fiber optic biosensors can be an effective way to diagnose diabetic retinopathy in the early stages. Fiber optic biosensors, such as semi-distributed interferometers have simple, fast and low-cost sensor fabrication technology, which makes them very attractive for use in the field of diagnostics. In the course of this study, semi-distributed interferometric (SDI) sensors for the detection of diabetic retinopathy biomarkers LCN1 and VEGF were developed. In the process of optimizing sensor development, it was determined that the LCN1 sensor works most effectively when it is functionalized at a concentration of anti-LCN1 antibodies of 8 µg/ml. The optimal concentration for sensor functionalization for VEGF was 10 µg/mlItem Restricted ENVIRONMENTAL ASSESSMENT OF WATER TREATMENT SLUDGE DISPOSAL(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-30) Alibekov, AlisherSludge disposal is often overlooked when performing a Life Cycle Assessment (LCA) of water treatment plants (WTP). This study considered two common WTP sludge disposal methods (dehydration-landfilling and direct discharge to the water bodies) to determine their contribution to the overall WTP impact. The WTP LCA model, including the sludge disposal, was built in SimaPro 9.4.0.1, and the environmental impact was calculated using the ReCiPe 2016 method. The results indicate that landfilling can contribute 9.29-86.8% and 39.31-52.66% of the impact in all midpoint and endpoint categories, respectively. On the contrary, the discharge system contributes only to 5 out of 18 considered midpoint categories. Still, it is responsible for at least 93.6% of the endpoint impact in damage to human health and ecosystems categories. The sensitivity analysis revealed that the share of WTP sludge disposal can fluctuate significantly for the landfill system but remains the same for discharge cases under varying input parameters. The main reason for such high impact scores for both cases can be the contaminants in sludge, namely Arsenic, Zinc, and Nickel. Generally, the findings of this study indicate that sludge treatment and disposal is the vital element of the WTP life cycle inventory.Item Restricted EFFICIENT P-RECOVERY FROM THERMOCHEMICAL CONVERSION OF SEWAGE SLUDGE(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-23) Meruyert, AlisherSewage sludge is a by-product of wastewater treatment plants that contains high concentrations of essential nutrients including phosphorus (P). Considering limited P-resources, its recovery from the sludge is a sustainable method for phosphorus recycling. However, the recovery process is limited by the presence of toxic heavy metals available in the sewage sludge. Among various methods to efficiently recover phosphorus, incineration of sewage sludge spiked with chlorine-donor compounds has been useful for toxic heavy metal removal in the ash. In this study, effect of addition of single and mixed Cl-donor salts to the sewage sludge, prior to incineration, have been analyzed in a muffle furnace and bench scale bubbling fluidized bed (BFB) reactor. Additionally, Ca(OH)2 was used in combination with Cl-salts to gain a broader understanding of the process and improve Cr and Ni removal. Initially, the sewage sludge and ash samples were characterized by thermogravimetric analysis (TGA), inductively coupled plasma mass spectrometry (ICP-MS), and X-ray diffraction (XRD). Phosphorus content in the residual ash was 11% by weight. Further, samples of sewage sludge were doped with CaCl2, NH4Cl, NaCl, KCl, and MgCl2 and their mixtures. Presence of salts led to a decrease in Cr (by 57%), Ni (by 59%), Cu (by 57%), Pb (by 95%), Cd (by 93%), As (by 83%), and Zn (by 98%). It was observed that CaCl2, KCl and MgCl2 were more efficient than NH4Cl and NaCl in decreasing the heavy metal content in the ash. Moreover, incineration of sewage sludge in the presence of double salt mixtures was less efficient than incineration with single salt. However, substitution of CaCl2 in a KCl and CaCl2 combination with Ca(OH)2 improved Cr and Ni removal by 10-15% compared to both single and combined salts. Observations also showed better efficiency of the muffle furnace in heavy metal removal as compared to the BFB reactor.