Oil-based Production Research Articles

Response surface optimization of a single-step castor oil-based biodiesel production process using a stator-rotor hydrodynamic cavitation reactor.

In order to combat environmental pollution and the depletion of non-renewable fuels, feasible, eco-friendly, and sustainable biodiesel production from non-edible oil crops must be augmented. This study is the first to intensify biodiesel production from castor oil using a self-manufactured cylindrical stator-rotor hydrodynamic cavitation reactor. In order to model and optimize the biodiesel yield, a response surface methodology based on a 1/2 fraction-three-level face center composite design of three levels and five experimental factors was used. The predicted ideal operating parameters were found to be 52.51°C, 1164.8rpm rotor speed, 27.43min, 8.4:1 methanol-to-oil molar ratio, and 0.89% KOH concentration. That yielded 95.51% biodiesel with a 99% fatty acid methyl ester content. It recorded a relatively low energy consumption and high cavitation yield of 6.09 × 105J and 12 × 10-3g/J, respectively. The generated biodiesel and bio-/petro-diesel blends had good fuel qualities that were on par with global norms and commercially available Egyptian petro-diesel. The preliminary cost analysis assured the feasibility of the applied process.

Soybean oil-based green diesel production via catalytic deoxygenation (CDO) technology using low-cost modified dolomite and commercial zeolite-based catalyst

Green diesel derived from sustainable biomass is an alternative and potential energy source to petroleum fossil fuel replacement in response to reducing carbon footprint and achieving a circular economy, which has sparked public interest and concern in advancing renewable energy development. Catalytic deoxygenation (CDO) is a promising method because it can process a wide variety of feedstocks and produce a diverse range of fuels. The CDO of soybean oil (SO) was executed using a modified low-cost dolomite catalyst denoted as NiO-CD catalyst and its performance has been compared with commercial zeolite heterogeneous-based catalysts such as ZSM-5, HY-zeolite and FCC. The NiO-CD catalyst exhibited exceptional deoxygenation ability, attaining an 88.6 % removal efficiency of oxygenated compounds, markedly surpassing all commercially available zeolite catalysts. The highest degree of CDO of SO via decarboxylation/decarbonylation (deCOx) reaction was achieved due to improvement in particle size, mesoporous structure and the presence of the synergistic effect of modified bi-functional acid-base properties of NiO-CaO/MgO catalyst. To investigate the effect of NiO-CD catalyst loading ranging from 1 to 7 wt%, a One Factor At a Time (OFAT) optimisation study was performed. The current study found that an optimised NiO-CD catalyst loading of 5 wt% yielded the highest green diesel (50.5 wt%) with an 88.63 % hydrocarbon composition. The influence of catalyst loading on deoxygenation activity is significant in green diesel production using NiO-CD catalyst.

Saccharomyces cerevisiae strains performing similarly during fermentation of lignocellulosic hydrolysates show pronounced differences in transcriptional stress responses.

The need for sustainable alternatives to oil-based production of biochemicals and biofuels is undisputable. Saccharomyces cerevisiae is the most commonly used industrial fermentation workhorse. The fermentation of lignocellulosic hydrolysates, second-generation biomass unsuited for food and feed, is still hampered by lowered productivities as the raw material is inhibitory for the cells. In order to map the genetic responses of different S. cerevisiae strains, we performed RNA sequencing of a CEN.PK laboratory strain, two industrial strains (KE6-12 and Ethanol Red), and two wild-type isolates of the LBCM collection when cultivated anaerobically in wheat straw hydrolysate. While the response to inhibitors of S. cerevisiae has been studied earlier, this has in previous studies been done in aerobic conditions. The transcriptomic analysis highlights different evolutionary adaptations among the different S. cerevisiae strains and suggests novel gene targets for improving fermentation performance and robustness.

Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models.

Lubricants are important fluids and are commonly used to suppress friction between two metallic surfaces and as a medium for heat transportation. In an industrial plant considered in this study, the base oil mode changes can only be detected based on the kinematic viscosity values obtained using lab analysis. Since the lab analysis data are only available every 8 h, detecting the change in the production modes for 4, 6, and 10 cSt and the transitions among them are significantly delayed, causing unnecessary off-spec products that have to be directed to the slopping tank. In this paper, the innovativeness of the work comes from the idea of trying to unravel the underlying pattern of the plant data that correlate to the changes in the base oil modes and using that to classify hourly the kinematic viscosity values. Hence, a novel industrial application is presented to predict the class of base oil mode change on an hourly basis that can significantly reduce the losses in terms of off spec products and sloping tank wastes. The modes are segregated into three classes based on the values of kinematic viscosity. The classes are C-1 (4 cSt), C-2 (6 cSt), and C-3 (10 cSt). Anything in between the stipulated thresholds is called transition [T-12 (C-1 to C-2), T-21(C-2 to C-1), T-23 (C-2 to C-3), T-31 (C-3 to C-1), and T-32 (C-3 to C-2)]. To unravel the pattern, principal component analysis (PCA) is utilized on 42,000 operating plant data. After a thorough analysis, the third principal component provides the highest correlation to the eight classes of the base oil mode changes [C-1 (4 cSt), C-2 (6 cSt), and C-3 (10 cSt) and the transitions T-12 (C-1 to C-2), T-21(C-2 to C-1), T-23 (C-2 to C-3), T-31 (C-3 to C-1), and T-32 (C-3 to C-2)]. This third principal component is then utilized together with plant process variable values as inputs to four machine learning models, namely, XGBOOST, Random Forest, and CatBoost algorithms to predict the mode of the base oil hourly. The overall comparison analysis shows that utilizing the XGBoost algorithm for the prediction of the eight classes of the base oil modes at a faster hourly rate results in the most consistent classification accuracy of 92.96% for the test set and 89.22% in the deployment set. This capability to predict the mode change in the hourly basis can significantly reduce the losses in terms of off spec products in the production line.

Process intensified lauric acid self-ketonization and its economic and environmental impact on biolubricant base oil production

Intensified lauric acid self-ketonization and its impact on biolubricant base oil production.

ПРОИЗВОДСТВО БАЗОВЫХ МАСЕЛ В РОССИИ

The article discusses the modern production of base oils, their types, types, methods of produc-tion and classification. Their compositional diversity is represented on the basis of a variety of types of commercial oils. The list of oil refineries producing base oils on the territory of Russia is presented, as well as the basic production schemes of some of them are considered

Machine Learning-Based Modeling and Optimization Analysis for an Integrated Industrial Base Oil Production Complex

Machine Learning-Based Modeling and Optimization Analysis for an Integrated Industrial Base Oil Production Complex

MMP Net: A feedforward neural network model with sequential inputs for representing continuous multistage manufacturing processes without intermediate outputs

Machine learning models that are used for the prediction and control of production can improve quality and yield. However, developing models that are highly accurate and reflective of real-world processes is challenging. We propose a feedforward neural network model specifically designed for continuous Multistage Manufacturing Processes (MMPs) without intermediate outputs. This model, which is termed “MMP Net,” can accurately represent the control mechanism of continuous MMPs. Whereas existing studies on learning MMPs assume an intermediate output data, the MMP Net does not require such an unrealistic assumption. We use the MMP Net to develop prediction models for the lubricant base oil production process of a world-leading lubricant manufacturer. Evaluation results show that the MMP Net is superior to other deep neural network and machine learning models. Consequently, the MMP Net was actually implemented in a real factory in 2022 and is expected to save 900,000 dollars per year for each production line. We believe that our work can serve as a basis to develop customized machine learning solutions for improving continuous MMPs.

Diatomite reinforced modified safflower oil-based epoxy biocomposite production: Optimization with RSM and assessment of outcomes by ANN

In this study, a biocomposite based on modified safflower oil (MSO) has been created and its structure has been reinforced with diatomite. The weight of the petrochemical raw material utilized is decreased with the usage of MSO, and diatomite is employed to create a novel, environmentally friendly biocomposite. Response surface methodology (RSM) is used to optimize experimental research while taking the resulting biocomposite's thermophysical properties into consideration. The chemical bond structure of the biocomposite is examined using Fourier transform infrared (FTIR) spectroscopy. For thermal decomposition behavior, thermogravimetric analysis (TGA) is performed, and scanning electron microscopy (SEM) is used for surface morphology. Additionally, research has been done on the diatomite-reinforced MSO-based biocomposite's Shore D hardness, density, and thermal conductivity coefficient. It has been found that MSO interacts well with diatomite to improve some of the biocomposite's features and to help it create new ones. The employment of an artificial neural network (ANN) and RSM has been shown to facilitate the effective and efficient execution of experimental research and the more accurate evaluation of results. According to RSM, biocomposite production with 65 wt% epoxy A, 34 wt% epoxy B, 8 wt% MSO, and 5 wt% diatomite is optimum.

Electrical dewaxing of the oil raffinate fraction 420-490 оС

Industrial dewaxing of oil raffinates remains the most expensive, complex, low-temperature process in the production of mineral base oils in refining. The development of an alternative, simpler and less energy-intensive method of dewaxing is an urgent task.This article investigates the possibility of dewaxing oil refining in the presence of different classes of depressor additives in a constant inhomogeneous electric field at positive temperatures.A correlation was established between the depressant properties of the additives and their effectiveness in electric field dewaxing.The maximum depressant effect of the additive in the oil raffinate corresponds to the highest indicators of the electrical dewaxing process.The effect of separating oil raffinate in an electric field with ester additives depends on the chemical structure of the dicarboxylic acids used in their synthesis.The nature of the distribution of the additive between the phases has been revealed by changes in the pour point of the resulting dewaxed oil.This article shows the relationship between the magnitude of the electrical charge of paraffin crystals and the time of dewaxing. This is related to the polarisation of the double electric layer of particles in an electric field and their subsequent dipole-dipole interaction.

Microalgae Oil-Based Metal Working Fluids for Sustainable Minimum Quantity Lubrication (MQL) Operations—A Perspective

This article presents a perspective on the potential use of microalgae oils in the production of metal working fluids (MWFs) used for minimum quantity lubrication (MQL) operations. The generalities of MQL operations and requirements of MWFs, and current advances in the development of the most promising microalgae oils with high contents of saturated, monounsaturated, and polyunsaturated fatty acids were reviewed and discussed. The analysis of data, discussions, and conclusions of numerous studies published recently and combined with the experience of the multidisciplinary team of authors strongly suggest that microalgae oils do indeed have great potential as sustainable and eco-friendly base oils for producing semi-synthetic MWFs, soluble oils and straight cutting fluids for MQL operations. Additionally, gaps and challenges focused on the use of agro-industry wastewater in microalgae production, green harvesting and oil extraction methods, and replacement of toxic additives in MWFs by green nanoparticles and biopolymers were identified and highlighted for achieving massive microalgae oil-based MWFs production and truly green machining processes.

Post-harvest waste to value-added materials: Musa champa plant as renewable and highly effective base catalyst for Jatropha curcas oil-based biodiesel production

In the present work, different catalysts were prepared from the post-harvest stem, rhizome and fruit peel of Musa champa plant and applied in biodiesel production from Jatropha curcas oil. The calcined Musa champa peel (CMCP-550) catalyst contained the highest amount of K (47.49 wt%) and exhibited the highest basicity of 1.25 mmol g−1, which in turn showed the best efficacy for the reaction in comparison to other prepared catalysts of this work. The CMCP-550 catalyst with a surface area of 6.848 m2 g−1 could produce a maximum biodiesel yield of 98.27 % at the optimum reaction conditions (ORCs) of 9:1 MRMO (molar ratio of methanol to oil) and 5 wt% of catalyst dose at 65 °C in 10 min. The CMCP-550 catalyzed reaction exhibited good activation energy of 54.256 kJ mol−1 with a turnover frequency (TOF) of 14.15 h−1.

Experimental and theoretical feasibility study of methanol application for Echium oil-based biodiesel production

In this study, the effect of ultrasound waves on biodiesel production from methanol and Echium oil is evaluated experimentally under different operating conditions. A two-step esterification–transesterification process was applied to improve the biodiesel quality, and, in order to optimize the reaction conditions, the effect of variables such as methanol to oil molar ratio, KOH catalyst concentration and temperature were experimentally investigated. The results showed that the optimum biodiesel yield (97.2%) could be obtained at a catalyst concentration of 1.875% w.t, feed molar ratio equal to 9.65, and at 50 °C of operating temperature. The acid value, saponification numbers, iodine value, density, kinematic viscosity, fire point, cloud point and flash point were measured and compared with ASTM D6751 and EN 14214 standards. The results of the analysis showed that the produced biodiesel is a good alternative to fossil fuels. The results demonstrate that methanol and Echium Oil can be used as a proper feedstock for biofuel production. Furthermore, the kinetic study of transesterification is carried out for Echium oil production and the reaction rate constant and activation energy were determined. Finally, the sizing calculations and cost estimation of the Echium oil-based biodiesel production were investigated.

Life cycle assessment and economic analysis of HFC-134a production from natural gas compared with oil-based and coal-based production

Life cycle assessment and economic analysis of HFC-134a production from natural gas compared with oil-based and coal-based production

Mesophase pitch production from fluorine-pretreated FCC decant oil

• Effect of fluorine treatment over FCC-DO for mesophase formation firstly suggested. • Reaction mechanism of mesophase formation by fluorination was suggested. • Fluorine promoted radical polymerization of FCC-DO to form heavy mesophase nuclei. • The amount of hydrogen donating species also increased after the fluorination. • Pitch from fluorinated DO had higher mesophase yield than pitch from pristine DO. Petroleum residue oil-based mesophase pitch production suffers from its low yield of mesophase. There have been continuous efforts, such as catalyst-assisted reactions and multistep heat treatments, to produce high-purity mesophase pitch. However, most of these techniques have difficulties with the processability. In this study, we suggest a novel fluorine pretreatment of fluid catalytic cracking (FCC) decant oil for mesophase pitch (MP) production. The effects of fluorine pretreatment on FCC decant oil were evaluated with mass spectrometry (MS) and their hydrogen donating ability. Subsequently, the MPs from pristine pitches with fluorine pretreatment were intensively compared in terms of structural, thermal, and chemical characteristics. From these results, it was found that fluorine pretreatment helps mesophase formation by catalytic radical polymerization but does not cause coking and does not require posttreatment. It was found that the fluorination method can also provide an effective method for generating MP by increasing the hydrogen donating ability (HDA) fraction. We believe that the results of this work could inspire new insights into MP production.

Development of inferential sensor and real-time optimizer for a vacuum distillation unit by recurrent neural network modeling of time series data

In lube base oil production, a vacuum distillation unit (VDU) is a crucial unit operation which must be controlled tightly to meet final product specifications. To address this requirement, model-based optimal control can be employed, which requires an accurate model relating to the operation and quality variables involved. However, the mathematical modeling of VDU is hampered by the lack of process knowledge and the limited online data acquisition capability for key quality variables. As a recourse, data-based modeling can be attempted to develop inferential sensors and controllers but one is faced with the problem of temporal credit assignment due to the complex characteristics of the VDU process dynamics, e.g., varying time-lags among the variables. To overcome this difficulty, the use of a stacked RNN structure with sequence length is proposed for the prediction of kinematic viscosity. In addition, regression models for the product's distillation temperatures are constructed by testing a large number of regression model structures. Then, these quality predictors are applied in developing a real-time optimizer which adjusts key operating variables in order to satisfy the product specifications despite various disturbances. Accuracies of the soft sensor and the optimization model are evaluated, first offline and then, online through a commercial plant test performed at SK Ulsan Refinery Complex in Korea. The overall approach and framework are general and can be used for the development of inferential sensors and controllers in other similar situations.

Biomass-derived nano-catalyst for biodiesel production from waste cooking oil

This study aimed to produce a nanocatalysts from inexpensive barley straw using nickel (Ni) and cobalt (Co) to support waste cooking oil-based biodiesel production. At 400 °C without oxygen and 1-5 bars of pressure, the gasification procedure of barley straw biomass (100g dry basis) was utilized in a muffle furnace with Ni and Co nano-catalysts. The biomass:Ni:Co catalyst mixing ratio is 1:1:1. The catalyst content and reaction time were applied for 2 hours. Then, at molar ratios of methanol:oil (6:1, 9:1, and 12:1) with the amount of catalyst (1, 2, and 3% weight percent basis), at 2 hours reaction time. Accordingly, the factors impacting the transesterification of biodiesel synthesis were evaluated. The process employing methanol:oil molar ratio of 6:1 and a catalyst quantity of 2% wt was the best for producing biodiesel. Based on the results of this study, nanocatalysts formed from biomass, which can be obtained from agricultural waste, hold commercial promise as a catalyst source for biodiesel.

Novel iron sand-derived α-Fe2O3/CaO2 bifunctional catalyst for waste cooking oil-based biodiesel production.

The main aim of this work was to develop a heterogeneous Fe2O3/CaO2 bifunctional catalyst prepared from iron sand and 3 different CaO2 sources (CaCO3, Ca (OH)2, and limestone) using wet impregnation and calcination methods for biodiesel production. The effects of different CaO2 sources and Fe/Ca ratio in the catalyst were investigated to provide insight into the catalyst character and biodiesel yield. X-ray diffraction, X-ray fluorescence, and scanning electron microscopy analyses were used to characterize the catalyst. CaCO3 was concluded as the best CaO2 source, while the best Fe/Ca configuration was found to be 1:4, giving the highest biodiesel yield (97.0401%) with no diglycerides. Greater addition of Fe loading would result in an amorphous structure, and all catalysts were relatively crystalline. Fe was concluded to favor the esterification reaction and biodiesel formation, while CaO2 was seen to favor the transesterification reaction and fatty acid methyl ester (FAME) formation. The catalyst mechanism was also established in this study, where esterification of free fatty acid (FFA) and glycerol took place on the acid site to produce diglyceride and transesterification of triglyceride by methanol occurred on the basic site.

Impacts of Palm Oil-based Biofuel Utilization Promotion Policy in the Thai Transport Sector

This paper aims to provide an analysis of the impacts of the promotion policy of palm oil-based biofuel utilization in the Thai transport sector for the period 2020–2037. For this purpose, three scenarios are developed to represent different proportions of palm oil-based biofuel blending implemented in Thailand and their impacts are analyzed through the application of the Low Emissions Analysis Platform (LEAP) model. The analysis reveals that an increase in the proportion of palm oil-based biofuel in a diesel mix would provide several noticeable benefits, for example, help reducing diesel consumption, decreasing crude oil requirement, and mitigating emissions of CO2 and PM2.5 – a major source of current environmental health problems. In addition, increased proportion of palm oil-based biofuel in the diesel mix would contribute to a growing demand for oil palm production and a plantation area to meet an increase in palm oil-based biofuel production. A high demand for oil palm production and plantation land requirement could, however, be emerging challenges. In order to address these challenges, the effective strategies could include a plan for agricultural crop zoning, crop breeding, the efficiency enhancement of biofuel conversion technology, the reduction of chemical fertilizers and pesticides, and the support from the government for research and development of second-generation biofuels. The analysis will be useful for Thai planners and policy makers to design policies to overcome the issues of energy and food security as well as climate change problems.

Optimization of biodiesel production from waste cooking oil using α-Fe2O3/HAP catalyst via simultaneous esterification-transesterification reaction

ABSTRACT The study aimed to optimize a waste-cooking oil-based biodiesel production process using α-Fe2O3/Hydroxyapatite (HAP) catalyst from natural iron sand and phosphate rocks. The parameters investigated in this study were reaction temperature, methanol:WCO molar ratio, and catalyst weight. Response surface methodology (RSM) with central composite design (CCD) was used to design the experiment, and a model in the form of a quadratic equation was later generated based on the investigated parameters and the observed biodiesel yield response. The model was subjected to ANOVA analysis to determine its validity and the influence of the investigated parameters on the observed response. The results showed that the catalyst weight was the only parameter significantly influencing the biodiesel yield, although the reaction temperature was concluded to be borderline significant. The optimal reaction condition suggested was 70°C reaction temperature with 3.68%w/w catalyst weight and 10.47 mol/mol methanol:WCO molar ratio to achieve 93.96% biodiesel yield.