Refinery Process Research Articles

Simulation of Nigerian Crude Oil Types for Modular Refinery (Topping Plant) Operations

This research study focused on the need to curb scarcity and importation of petroleum finished products in oil-producing nation Nigeria through the operation of conventional modular refineries in conjunction with major refineries operating efficiently. Hence, the study focused on the suitability and operations of conventional modular refinery processes by considering twenty different types of Nigerian crude oil for crude oil assay analysis and classification using Aspen Hysys. The crude oil assay results categorized the twenty Nigerian crude oil types as light and medium sweet crude, while based on recovery volume percent at a true boiling point of 370℃, the twenty crude oil types were categorized into Group A (crude oil with recovery volume above 80%), Group B (crude oil with recovery volume between 70% and 79%) and Group C (crude oil with recovery volume below 70%) respectively. Besides, light and medium sweet oil types were simulated in a conventional modular refinery (topping plant) at different numbers of column trays (25, 29, 35, 40 and 48) to determine their product yield. Based on product yield and equipment costs at different numbers of tray columns, a modular refinery with twenty-nine column trays was applied in this study. Thus, twenty Nigerian crude oil types were simulated in a conventional modular refinery of 30,000 barrel per day capacity and twenty-nine column trays respectively to evaluate their product yield and tray compositions.

Optimization of Distribution Control System in Oil Refinery by Applying Hybrid Machine Learning Techniques

In this research, prediction of crude oil cuts from the first stage of refining process field is laid out using rough set theory (RST) based adaptive neuro-fuzzy inference system (ANFIS) soft sensor model to enhance the performance of oil refinery process. The RST was used to reduce the fuzzy rule sets of ANFIS model, and its features in the decision table. Also, discretisation methods were used to optimise the continuous data’s discretisation. This helps to predict the two critical variables of light naphtha product: Reid Vapor Pressure (RVP) and American Petroleum Institute gravity (API gravity), which detect the cut’s quality. Hence, a real-time process of Al Doura oil refinery is examined and the process data of refining crude oil from these two sources improve the knowledge provided by the data. The response variables represent the feedback measured value of cascade controller in the top of the splitter in crude distillation unit (CDU) in the rectifying section, which controls the reflux liquid’s flow towards the splitter’s head. The proposed adaptive soft sensor model succeeded to fit the results from laboratory tests, and a steady-state control system was achieved through an embedded virtual sensor. The predictive control system has been employed using cascade ANFIS controller in parallel with the soft sensor model to keep the purity of the distillate product in the stated range of the quality control of oil refinery. The results obtained from the proposed ANFIS based cascade control have no over/undershoots, and the rise time and settling time are improved by 26.65% and 84.63%, respectively than the conventional proportional-integral-derivative (PID) based cascade control. Furthermore, the results of prediction and control model are compared with those of other machine learning techniques.

Implementasi Realitas Berimbuh pada Antarmuka Manusia-Mesin di Industri Proses

Process industries such as oil refineries, petrochemical plants, and power plants require a human-machine interface system to monitor continuously. The operator usually carries out monitoring via a human-machine interface. However, it is difficult to know the condition of process equipment in real-time. The implementation of augmented reality allows engineers to visualize process equipment in real-time when conducting field inspections. The implementation of augmented reality at the human-machine interface to the fluid catalytic cracking process in an oil refinery is discussed in this paper. The design was started by developing a three-dimensional process equipment model using Autodesk Inventor. The result of the three-dimensional model then using Unity 3D software connected to the Vuforia Engine was implemented on a gadget into an augmented reality application. Data communication performance analysis was carried out using inferential statistics methods to test variations in service quality at levels 0, 1, and 2. The result of the Tukey test showed that the communication network latency value in level 2 was significantly higher than levels 0 and 1, which was 0.704±0.108 seconds. These results indicate that augmented reality can be implemented on human-machine interfaces by ensuring the quality of data communication services using Message Queue Telemetry Transport (MQTT) protocol at levels 0 or 1.

Ağır Aromatik Atık ve Polistirenin Eş-pirolizi: Sıvı Ürünün GC-MS ile Analizi

Plastic wastes forms 18.46 % of total municipal solid wastes in 2018 around the world. Heavy hydrocarbons occur as the waste of refinery processes. In this study co-pyrolysis of waste polystyrene foam (PS) and heavy aromatic waste (HAW) was carried out for the first time in literature. It was aimed to investigate the effect of temperature (420, 450, 480 ⁰C) and mixing ratio (1/5, 3/5, 1/1) on liquid product yield and compound distribution in the pyrolytic liquids. Whole experiments were done in semi-batch reactor configuration under nitrogen atmosphere. Gas chromatography-Mass spectrometry (GC-MS) was utilized to analyze the pyrolytic liquids. At 480 ⁰C it was obtained maximum liquid yield as 87.24 % in 1/5 HAW/PS feedstock ratio. Compared to the pure PS pyrolysis, it was achieved relatively low styrene, toluene yield. However yields of ethyl benzene and cumene increased with HAW adding to the feed. Naphthalene was not detected in liquid of PS pyrolysis. Maximum naphthalene yield was taken as 12.71% at 480 ⁰C with 1/5 HAW/PS feedstock ratio.

Investigating the Correlation of Microplastic Pollution Between Seawater and Marine Salt Using Micro-Raman Spectroscopy

Microplastics (MPs) are synthetic polymer pieces with a size of less than 5 mm that are ubiquitous in the marine environment. They have been recently detected in several wild species and sea products. An indispensable condiment in human food is marine salt that is produced from seawater. Since toxicity studies report potential health impacts when plastic debris is ingested, more attention must be paid to the MP contamination of sea salt and the underlying reasons for this. The central aim of this study is to evaluate the MPs contamination level of sea salt in correlation with the MPs contamination level of seawater in Vietnamese areas. Micro-Raman spectroscopy was employed to determine MPs in the samples collected from three artificial salt pans of Vietnam. The result revealed the presence of MPs in all study areas—Vung Tau (VT;14.54 MPs/L seawater and 114.67 MPs/kg salt), Ly Nhon (LN; 13.14 MPs/L seawater and 63.59 MPs/kg salt), and Can Thanh (9.42 MPs/L seawater and 93.69 MPs/kg salt). The comparisons highlight close correlations in the percentage, shape, size, and color of MPs, especially polyethylene, polyethylene terephthalate, and polypropylene particles extracted from the seawater and its salt. This study proved that seawater is a plastic pollution source for salts produced by traditional seawater evaporation. The study also alerts the prevalence of MPs in the environment and human consumables, thereby indicating that actions must be taken to reduce the pollution of water sources in Vung Tau and at the UNESCO Can Gio Mangrove Biosphere Reserve and improve salt production and refinery processes in order to minimize the number of MPs in final salt products for safe consumption.

Basic Refinery Training Courseware in Enhancing Technical Employees’ Visualization Skills

Various research has been conducted to enhance the employees’ visualization skills in different areas and the importance and the role of visualization skills towards the employee’s achievement, especially in the industry such as manufacturing, medical, oil and gas field, and many more. To improve visualization skills, the use of effective training methods and tools should be applied to facilitate and enhance the understanding and performance in general, in a way to help them solve problems at the workplace. Thus, the aim of this paper focuses to the attention on the practical use of augmented reality (AR) with the ability of digital mobile application in the crude oil refinery process within the oil and gas industry environment, describing the potential and the effectiveness of the technology to enhance the visualization skill among the technical employees. This study used Basic refinery courseware that can be accessed via digital network to give an interactive training experience that can enhance technical employees' visualization skills. This courseware shows the positive effect and impact on technical employees’ visualization skills and performance based on the results. The results have also shown an increased motivation, making them more cooperative and developing visualization skills more quickly. Considering these results, it can be concluded that Basic Refinery training courseware based on augmented reality application is an effective and beneficial tool to be applied in training programs among the workplace's technical employees.

Modeling of nitrogen solubility in normal alkanes using machine learning methods compared with cubic and PC-SAFT equations of state

Accurate prediction of the solubility of gases in hydrocarbons is a crucial factor in designing enhanced oil recovery (EOR) operations by gas injection as well as separation, and chemical reaction processes in a petroleum refinery. In this work, nitrogen (N2) solubility in normal alkanes as the major constituents of crude oil was modeled using five representative machine learning (ML) models namely gradient boosting with categorical features support (CatBoost), random forest, light gradient boosting machine (LightGBM), k-nearest neighbors (k-NN), and extreme gradient boosting (XGBoost). A large solubility databank containing 1982 data points was utilized to establish the models for predicting N2 solubility in normal alkanes as a function of pressure, temperature, and molecular weight of normal alkanes over broad ranges of operating pressure (0.0212–69.12 MPa) and temperature (91–703 K). The molecular weight range of normal alkanes was from 16 to 507 g/mol. Also, five equations of state (EOSs) including Redlich–Kwong (RK), Soave–Redlich–Kwong (SRK), Zudkevitch–Joffe (ZJ), Peng–Robinson (PR), and perturbed-chain statistical associating fluid theory (PC-SAFT) were used comparatively with the ML models to estimate N2 solubility in normal alkanes. Results revealed that the CatBoost model is the most precise model in this work with a root mean square error of 0.0147 and coefficient of determination of 0.9943. ZJ EOS also provided the best estimates for the N2 solubility in normal alkanes among the EOSs. Lastly, the results of relevancy factor analysis indicated that pressure has the greatest influence on N2 solubility in normal alkanes and the N2 solubility increases with increasing the molecular weight of normal alkanes.

Bioenergy as a prospective energy source in the future

The issues of global warming and greenhouse gasses have raised the concern of the people and led to integrate ambitious of using bioenergy in many countries. In this paper, the issues of conventional energy are stated, and the different types of conventional energy resources are discussed including coal, petroleum and natural gas. The process of pulverized coal combustion (PCC) to generate electricity is also studied and discussed in this paper to have a better understanding of the process and the emission of greenhouse gas released from the use of coal to generate conventional energy. The reason of soil contamination and air pollution caused by the refinery process of petroleum is also discussed in this paper and several study cases of the social economic impact caused by the production of natural gas in development countries. Furthermore, the used of bioenergy and different type of bioenergy resource are discussed including biogas, algae biofuel, and biodiesel. The process of algae cultivation for algae biofuel and the study of toxic waste algae strains that can give a negative impact to the environment is studied to avoid harmful substances released to the environment. The potential algae application in different fields is also included to show the benefit of algae biofuel which is flexible and able to contribute to the global economic growth. Lastly, the advantages of using bioenergy are discussed including the mitigation of greenhouse gas emission, improve social economic growth, renewable energy resources and prevent prescribed burning of the forest.

Groundwater and society: fresh submarine groundwater discharge and itsmanagement improvement

The issues of global warming and greenhouse gasses have raised the concern of the people and led to integrate ambitious of using bioenergy in many countries. In this paper, the issues of conventional energy are stated, and the different types of conventional energy resources are discussed including coal, petroleum and natural gas. The process of pulverized coal combustion (PCC) to generate electricity is also studied and discussed in this paper to have a better understanding of the process and the emission of greenhouse gas released from the use of coal to generate conventional energy. The reason of soil contamination and air pollution caused by the refinery process of petroleum is also discussed in this paper and several study cases of the social economic impact caused by the production of natural gas in development countries. Furthermore, the used of bioenergy and different type of bioenergy resource are discussed including biogas, algae biofuel, and biodiesel. The process of algae cultivation for algae biofuel and the study of toxic waste algae strains that can give a negative impact to the environment is studied to avoid harmful substances released to the environment. The potential algae application in different fields is also included to show the benefit of algae biofuel which is flexible and able to contribute to the global economic growth. Lastly, the advantages of using bioenergy are discussed including the mitigation of greenhouse gas emission, improve social economic growth, renewable energy resources and prevent prescribed burning of the forest.

Optimal scheduling for simultaneous refinery manufacturing and multi oil-product pipeline distribution

Refinery manufacturing and the downstream oil-product distribution should be highly integrated in the refinery supply chain system. However, systematic studies dealing with simultaneous optimization of these two sectors are still lacking. In this paper, a general scheduling model for simultaneous optimization of refinery manufacturing and the downstream multi oil-product pipeline distribution (RM&MOPD) has been developed. It consists of three sub-models: (i) a refinery manufacturing (RM) sub-model covering all major refinery processing units such as crude distillation, hydrocracking, hydrotreating, and blending operations; (ii) a new multi oil-product pipeline distribution (MOPD) sub-model that considers comprehensive handling measures for oil transmix in the pipeline including downgrading, blending, and distillation operations; and (iii) a joint sub-model coupling the upstream refinery manufacturing and the downstream oil-product pipeline distribution. The developed RM&MOPD is a large-scale continuous-time and continuous-volume based MINLP model, where the objective is to minimize the total operating cost subject to various constraints such as operation and product specifications, inventory limits, delivery constraints, and oil-product demands at each oil depot. It can simultaneously provide optimal solutions for refinery unit operations, oil-product inventory management at refinery and depots, oil slug movement profiles inside pipeline, as well as oil-product feeding and discharging strategies from pipeline to multiple tanks at different depots.

Integrated sol-gel and hydrothermal synthesis of V2O5–TiO2 nanocatalysts for enhanced catalytic removal of H2S

Hydrogen sulfide (H2S) emitted from the petroleum refinery process, landfills, or biogas purification is severely corrosive and has harmful health effects on the human body when inhaled at even small concentrations. Selective catalytic oxidation using V2O5–TiO2 is considered to be a benchmark technology for removing H2S. This paper presents a novel preparation method to synthesize V2O5–TiO2 nanocatalysts to improve desulfurization performance. Compared with the conventional sol-gel or wet-impregnation method, the integrated sol-gel and hydrothermal synthesis allows for a substantial increase in the specific surface area, which is mainly attributed to the highly reduced sizes of the primary particles. The enhanced surface area contributes to effective dispersion of the vanadium (V) species on the TiO2 support, by which the interactions between V and titanium (Ti) are strengthened. Accordingly, the redox properties of the catalysts are improved with increased concentrations of the chemisorbed oxygen species on the catalyst surfaces. Finally, the properties of the V2O5–TiO2 nanocatalysts are improved by the new preparation conditions, resulting in significantly enhanced catalytic activities in the selective oxidation of H2S: the best catalyst exhibits excellent activity even at 50 °C. It was also observed that the temperature during the hydrothermal reaction severely affected the catalytic performance of the V2O5–TiO2 nanocatalysts. This study suggests the importance of the preparation conditions for manipulating the physicochemical properties and performances of catalysts.

Incorporating Future Climate Scenarios in Oil Industry’s Risk Assessment: A Greek Refinery Case Study

The impacts of climate change are anticipated to become stronger in the future, leading to higher costs and more severe accidents in the oil industry’s facilities and surrounding communities. Motivated by this, the main objective of this paper is to develop, for the oil industry, a risk assessment methodology that considers future climate projections. In the context of an action research effort, carried out in a refinery in Greece, we adapted the organization’s extant risk management approach based on the Risk Assessment Matrix (RAM) and suggested a risk quantification process that incorporates future climate projections. The Climate Risk Assessment Matrix (CRAM) was developed to be used to assess the exposure of the facility’s assets, including human resources, to future climate risks. To evaluate CRAM, a comparison with RAM for the specific organization for the period 1980–2004 was made. Next, the application of CRAM for the period 2025–2049 indicated that, even though the resilience of the operations of the company to extreme conditions seems adequate at present, increased attention should be paid in the future to the resilience of refinery processes, the cooling system, and human resources. Beyond the specific case, the paper provides lessons for similar organizations and infrastructures located elsewhere.

PHENOL removal in refinery wastewater using mixed oxides prepared by green synthesis

Mixed solid oxides are known for their excellent catalytic property and applications in environmental remediation. This study presents a green-synthesis route for magnesium oxide–titanium oxide, a mixed oxide here demonstrated to possess high performance of phenol removal from hydrocarbon refinery process wastewater. Mixed oxide (MgO-TiO2) was prepared by using the whole extract from leaves of Piliostigma Thonningii as reducing agent. A structural attribute of the mixed oxide was investigated using X-ray Diffractometer, High-Resolution Scanning Electronic Microscopy and Energy Dispersive X-ray. Petroleum refinery raw wastewater having phenol concentration of 19.961 mg/L was treated using the green-synthesized mixed oxide. Adsorptive phenols removal up to 99.5% was achieved with a dosage of 0.04 g/100 mL at temperature of 35 °C, and contact time of 1.167 h. By this, the treated water meets the standard acceptable phenol concentration (0.1 mg/L) in wastewater of hydrocarbon refinery.

Paradigm shift in algal biomass refinery and its challenges

Microalgae have been studied and tested for over 70 years. However, biodiesel, the prime target of the algal industry, has suffered from low competitiveness and current steps toward banning the internal combustion engine all over the world. Meanwhile, interest in reducing CO2 emissions has grown as the world has witnessed disasters caused by global warming. In this situation, in order to maximize the benefits of the microalgal industry and surmount current limitations, new breakthroughs are being sought. First, drop-in fuel, mandatory for the aviation and maritime industries, has been discussed as a new product. Second, methods to secure stable and feasible outdoor cultivation focusing on CO2 sequestration were investigated. Lastly, the need for an integrated refinery process to simultaneously produce multiple products has been discussed. While the merits of microalgae industry remain valid, further investigations into these new frontiers would put algal industry at the core of future bio-based economy.

Modeling of nitrogen solubility in unsaturated, cyclic, and aromatic hydrocarbons: Deep learning methods and SAFT equation of state

BackgroundAn accurate understanding of the nitrogen solubility in hydrocarbons is crucial for developing enhanced oil recovery (EOR) by nitrogen injection and the design of thermal separation and chemical conversion processes in chemical industries and oil refineries. Unsaturated, cyclic, and aromatic hydrocarbons as important compounds of crude oil are common solvents in the chemical industry that have received less attention compared to normal alkanes. MethodsIn this paper, four deep learning models, including recurrent neural network (RNN), long short-term memory (LSTM), deep belief network (DBN), and convolutional neural network (CNN) were developed for estimating the nitrogen solubility in unsaturated, cyclic, and aromatic hydrocarbons. To this end, 673 experimental nitrogen solubility data for 23 various hydrocarbons were collected from the literature in a wide ranges of operating pressure (0.03–100.1 MPa) and temperature (78–662.8 K). The input parameters to the models were considered critical temperature, critical pressure, and molecular weight of hydrocarbon solvents accompanying operating conditions of temperature and pressure. Also, the performance of deep learning models was compared with Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), and statistical associating fluid theory (SAFT) equations of state (EOSs). Significant findingsThe CNN model is able to estimate the experimental values of nitrogen solubility with a root mean square error (RMSE) of 0.0211. Also, SAFT EOS outperformed the two cubic EOSs. Based on sensitivity analysis, pressure has the greatest impact on nitrogen solubility in unsaturated, cyclic, and aromatic hydrocarbons, followed by temperature and the critical temperature of hydrocarbons. Nitrogen solubility in unsaturated, cyclic, and aromatic hydrocarbons increases with the increase in pressure and temperature, and it lowers by the increase in the critical pressure, critical temperature, and molecular weight of hydrocarbon solvents. Finally, high reliability of the experimental data and statistically high validity of the CNN model were proved by the Leverage approach. The findings of this study can have implications in increasing the efficiency of gas processing units, thermal separation, and chemical conversion processes.

Recycled valorization of bio-oil distillation residue via CO2-induced co-pyrolysis with biomass

Bio-oil rectification is gradually employed as an industrialized platform to recover energy and commodity chemicals, but bio-oil distillation residue (DR) as an undesired by-product severely hampers the continuous operation of biomass refinery process. In the present work, a recycled processing of DR was proposed by co-pyrolysis with walnut shell (WS) under CO2-induced atmospheres. The insights into co-pyrolytic mechanism, kinetic responses, and product distribution were further examined. Meanwhile, the development prospects of condensed bio-oils were outlooked for engineering and commercial applications. The results indicated that the co-pyrolytic interaction significantly suppressed the high-temperature pyrolysis, endowing a deteriorative mass synergy index from −0.63%/min to −1.91%/min. The impacts of induced atmospheres and DR fractions on the evolutions of activation energies satisfied cubic polynomials, and the activation energy ranged from 121.54 kJ/mol to 153.69 kJ/mol. Additionally, the CO2-mixed atmospheres mainly changed the kinetic mechanism at the early stage of co-pyrolysis (X <50%), while the addition of DR induced a modified mechanism for the late stage (X >50%). The two-dimensional correlation spectroscopy further demonstrated a lower CO2 concentration modified the primary sequential temperature responses of pyrogenic gases in the order: ethers/carboxylic acids/ketones < phenols < CO2/ethers < alcohols, whereas pure CO2 induced the responses as below: CO2 < carboxylic acids/ketones/ethers. More importantly, co-processing of DR and WS facilitated the hydrodeoxygenation reaction of pyrolytic vapors and promoted the production of phenol-rich bio-oils, and the CO2-assisted pyrolysis was conducive to the scissions of aromatics and enhancement of hydrocarbon yield. Generally, the presence of DR and CO2 effectively accelerated the aromatics cracks and hydrogenation of co-pyrolytic vapors, which provided feasibilities for the abundances of product composition and recoveries of commodity chemicals. This work also offered a possible strategy for downstream disposal of DR, which further laid a puissant basis on perfection of biomass refinery system.

Modeling of the fired preheater of crude oil considering the effect of geometrical parameters on fuel consumption

AbstractProminent equipment with a very high demand for fuels in refineries is the fired preheater of crude oil. There are two main reasons to investigate the energy usage of such fired heaters. First, this is essential to the economic value of the refinery process unit and second, this is located at the beginning of the process line, which causes inefficient performance or damage in downstream equipment if it is not tuned properly. This paper concentrates on the thermal modeling of a fired heater of crude oil to evaluate the effect of influential parameters on fuel consumption. As the number of variables is markedly high, and considering the complexity of thermal modeling, all equations in a code yield predefined heater efficiency. Results from the model show that the increase in heater size yields a decrease in efficiency. It was also found that with a constant dimension of the fired heater, change in the number of pipes is much less influential rather than more inexpensive ideas like increasing speed of the burning gas on reduction of the fuel consumption. In other words, it was found that a distance of 500–650 mm from the center to the center distance of the pipes is the optimal distance to reduce fuel consumption.

Propylene column pressure relief valves chattering resulting in explosion and fire of the Steam Cracker unit

The Refinery and petrochemical production facilities process and produce the gaseous and liquid flammable hydrocarbons under the pressure conditions. In case of any failure, improper design or human mistake there can be autonomous and independent pressure relieve systems activated to release the pressure to the flare systems to protect the equipment against the overpressure, damages and other consequences. The paper describes the failure occurred at the Steam Cracker unit in Litvinov, Czech Republic in 2015. As a result of a loss of cooling followed by loss of reflux event, the pressure relief system of the Propylene Column was activated whereas the pressure relief valves opened up and chattered, resulting in loosening of flange bolts and loss of containment. The release of the propylene vapor ignited, resulting in subsequent explosion and fire at the Propylene Column area. The interruption of steam and instrument air supply caused by the fire damages led to the other extensive fire of four pyrolysis heaters. The extraordinary event description, root cause analysis, investigation as well as the corrective actions are introduced and analyzed.

Water Contamination in Petroleum Refinery Processes

&lt;p&gt;This study was conducted to assessment the environmental impacts in petroleum refineries due to the different processes. The paper presents the data obtained during a case study was achieved in Tobruk petroleum refinery. The main petroleum products of the refinery represented by diesel, light naphtha, heavy naphtha and kerosene with maximum production capacity concerning 21,500 bbl/day. The results of the study revealed that the waste water affected by high concentration of hydrocarbons. The heavy metals are also determined and represented by V, Fe, Ni and Cu with low contents in crude oil. Also the pollutants parameters in the disposal water e.g. biochemical oxygen demand, chemical oxygen demand, total organic carbon, suspended solids, phenols, ammonia and sulphides are determined, and the results are compared with other refinery types. The concentration of these parameters are higher than the standard values that recommended by Environmental Protection Agency (EPA), at the same time these values are less if they are compared with the other refinery types.&lt;/p&gt;

Valorization of palm oil soap stock waste toward for biodiesel production: Process optimization under zero waste concept

Palm oil soap stock (POSS) was recycled from bio-waste to bio-fuel. Firstly, POSS was converted through acidification to be acidified POSS (APOSS) coupled with solvent extraction. APOSS with a high content of free fatty acids (FFAs) (90.94%) was obtained. Subsequently, the FFAs in APOSS were converted to biodiesel via esterification, catalyzed by immobilized alginate-polyvinyl alcohol (ALG-PVA) lipase. The key factors affecting the reaction, such as the temperature (30–50 °C), methanol to FFA molar ratio (2:1 to 4:1), and agitation rate (150–250 rpm) were optimized by statistical response surface methodology (RSM). The highest biodiesel yield was achieved with a reaction temperature of 40 °C, methanol/FFA molar ratio of 3:1, and 200 rpm agitation rate, as the optimal conditions. Furthermore, the biocatalyst can be reused up to 16 cycles. Interestingly, the alternative biodiesel production process reported herein promotes zero waste from the palm oil refinery process.