Processing Scheme Research Articles

Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel

Abstract: Traditional creep life prediction methods are generally difficult for researchers to fully consider the key factors affecting the creep performance, which limits their application in the research and development of new alloys. The artificial intelligence method can skip the complex mechanism and directly establish the mathematical correlation between the composition/process and the target performance. The accuracy, universality, and development efficiency of the model are better than the traditional material development strategy. In this study, we collected 216 creep data of austenitic heat-resistant steel, selected a variety of different machine learning algorithms to establish creep life prediction models, calculated and introduced a large amount of physical metallurgy knowledge highly related to creep based on Thermo-Calc, and converted the creep life into the form of the Larson–Miller parameter to optimize the data distribution, which effectively improved the prediction accuracy and interpretability of the model. In addition, the optimal model was combined with a genetic algorithm to obtain the best composition and process scheme with high-creep-performance potential, providing guidance for the design of austenitic heat-resistant steel.

Optimization of Underwater Images Based on Gray World Algorithm and Jaffe-McGlamery Models

In this paper, a comprehensive scheme for underwater image processing is proposed based on the grayscale world algorithm and the Jaffe-McGlamery model. Firstly, a color bias detection based on grayscale world theory, a low light detection based on HSV color space, and a fuzzy detection method based on frequency domain and Laplace operator are designed to classify different types of image degradation. Subsequently, the corresponding scene degradation models are constructed for different degradation types through the simplified Jaffe-McGlamery model, and the image features under different water conditions are analyzed. Next, an improved gray world algorithm is used to eliminate color bias, the limiting contrast adaptive histogram equalization (CLAHE) technique is utilized to improve the image quality under low-light conditions, and the dark-channel a priori algorithm is optimized by the depth estimation and parameter adaptation modules to remove blurring. The proposed method in this study significantly improves the image quality in different scenarios and provides a new idea for underwater image processing.

Intensification of Wastewater Treatment at Small Sewage Treatment Plants

The ways of intensification of domestic wastewater treatment processes at small wastewater treatment plants with capacity of 40 m3/day due to modernization of process scheme and application of bioaugmentation technology are considered. The directed selection of community with the introduction of fungal culture Penicillium brasilanum, which presumably plays the role of "biological framework" in the formation of aerobic sludge granule, was carried out. It is found that the isolated fungal culture contributes to the improvement of sedimentation properties. It is concluded that the introduction of fungal culture into the activated sludge community of wastewater treatment plants reduces the sludge index.

Modeling and Evaluation of Attention Mechanism Neural Network Based on Industrial Time Series Data

Chemical process control systems are complex, and modeling the controlled object is the first task in automatic control and optimal design. Most chemical process modeling experiments require test signals to be applied to the process, which may lead to production interruptions or cause safety accidents. Therefore, this paper proposes an improved transformer model based on a self-attention mechanism for modeling industrial processes. Then, an evaluation mechanism based on root mean square error (RMSE) and Kullback–Leibler divergence (KLD) metrics is designed to obtain more appropriate model parameters. The Variational Auto-Encoder (VAE) network is used to compute the associated KLD. Finally, a real nonlinear dynamic process in the petrochemical industry is modeled and evaluated using the proposed methodology to predict the time series data of the process. This study demonstrates the validity of the proposed transformer model and illustrates the versatility of using an integrated modeling, evaluation, and prediction scheme for nonlinear dynamic processes in process industries. The scheme is of great importance for the field of industrial soft measurements as well as for deep learning-based time series prediction. In addition, the issue of a suitable time domain for the prediction is discussed.

Life cycle assessment on the role of H2S-based hydrogen via H2S-methane reforming for the production of sustainable fuels.

Meeting current decarbonization targets requires a shift to a hydrogen energy nexus, yet, water is a valuable resource for hydrogen production, shifting the perspective to the use of H2S instead within the context of circular economy. A comprehensive understanding of the environmental impacts, using a cradle-to-gate life cycle assessment (LCA), was developed focusing on the operation of hydrogen sulfide-methane reforming (H2SMR) for H2 production benchmarked to conventional technologies, steam methane reforming (SMR) and SMR+carbon capture (CC), as feedstock to produce sustainable fuels (i.e., methanol and ammonia). The environmental impact of the different application routes was evaluated in terms of normalized impact categories and monetized indicators by calculating the environmental damage cost. The results indicated that the environmental impact increased when moving from H2SMR<SMR+CC<SMR, and ammonia compared to methanol production. Across all the processing schemes, the impact on human health is the largest based on the normalized values, representing 63.0-85.0% of endpoint level impacts. Within the scope of climate change, the use of H2SMR is indeed more supportive of climate mitigation efforts, reducing environmental costs related to GWP by 58.0% from SMR and 12.0% from SMR+CC. Addressing these concerns demands a comprehensive overhaul of existing practices within the oil and gas sector concerning raw material extraction, coupled with the implementation of effective waste management strategies to significantly minimize adverse environmental effects.

Low-carbon, green, and economic scheme for wastewater treatment process based on multi-objective optimal control

Low-carbon, green, and economic scheme for wastewater treatment process based on multi-objective optimal control

Conceptual design and environmental evaluation of the Biorefinery approach for R-phycoerythrin extraction and purification.

Marine algae are considered promising resources both at present and in the near future. Their availability, together with their molecular structure and properties, increases their applicability in various sectors: food and feed, cosmetics, pharmaceuticals and bioenergy. However, the "bio" qualification does not always imply a lower impact compared to fossil-based process schemes. Therefore, to verify the suitability of algae-based scenarios from a sustainable and circular perspective, it is necessary to assess their sustainability potential through process modelling (scaling up from laboratory scale to evaluate their potential at commercial level), environmental assessment (using the Life Cycle Assessment (LCA) method) and circularity analysis (by quantifying circularity indicators focusing on recovery, waste management and effective use of resources). In this context, this research report focused on the techno-economic assessment (TEA) and LCA of three alternative scenarios based on the extraction of R-phycoerythrin from offshore harvested macroalgae: water extraction followed by enzymatic digestion (S01), ultrasound-assisted extraction (S02) and water extraction (S03). In addition, the evaluation of environmental, social and circularity indicators and the application of the Greenness Grid methodology were included. According to the results obtained, S01 is the most promising alternative among the three scenarios due to its process productivity, lower environmental impact and potential sustainable scenario score according to the Green Chemistry assessment. Regarding the economic perspective, S03 is the only one that does not reach economic viability. Future studies should focus on improving process efficiency, promoting the use of renewable energy resources and supporting technological progress in emerging extraction processes.

Comparison of composite metal oxides as oxygen carrier for methane chemical looping reforming

Abstract The amount of greenhouse gases has increased considerably in recent years. Additionally, the energy required by humanity for daily activities is also on the rise. The planet is facing one of its worst crises, characterized by the overexploitation of fossil fuels due to population growth. It is estimated that by 2050, the global population will exceed 9 billion inhabitants. Chemical looping combustion (CLC), offers a potential solution. This process involves usually two interconnected reactors, usually with a fluidized bed, where combustion takes place in an alternate way. In this process, the oxygen required for combustion is provided by a solid oxygen carrier, the capacity of this depends on the nature of the material and is crucial to define the most effective one by a comparative study. Moreover, methane emissions are a significant concern, as methane is a potent greenhouse gas with a 25 times greater impact on the atmosphere compared to carbon dioxide as greenhouse gases. To address this, methane reforming in chemical cycles, such as Steam Reforming Chemical Looping Combustion (SR-CLC) or chemical looping reforming (CLR), is proposed. Using a Gibbs reactor and oxygen carrier data reported in the literature, the analysis of NiWO4, FeMoO4, Fe2MnO4 and FeZnO4, their operation, energy yield when exposed to a methane stream and the comparison between different forms of reforming schemes, as well as the estimation of the carrier needed for the process, are presented. Results indicate that after calculations, the g-carrier/g-fuel ratio for NiWO4 is almost 100 % higher than the other carriers studied in this work. Water vapor reforming generates 30.0930 kW and a stream of pure hydrogen that can be separated while carbon dioxide reforming is a general endothermic process that requires 12.22 kW of energy for this process scheme. Once the ideal carrier has been analyzed, the proposed future work should focus on the optimal design of a reaction system that will allow it to operate efficiently under the conditions encountered. In addition, it will be necessary to find the replacement rate for the carrier that will allow us to operate our system continuously.

Large-Bandwidth Lithium Niobate Electro-Optic Modulator for Frequency-Division Multiplexing RFID Systems

In the face of increasingly complex application scenarios, there is an urgent need for (Radio Frequency Identification) RFID systems that are capable of accurately identifying microwave signals of different frequency bands. Based on the acumen detection characteristics of microwave signals by lithium niobate electro-optic modulators, applying large-bandwidth thin-film lithium niobate electro-optic modulation to RFID systems can achieve efficient operation across multiple frequency bands. This study discusses, in detail, the design, simulation, fabrication, and testing process of the electro-optic modulator to obtain a high-performance, large-bandwidth lithium niobate electro-optic modulator. By using multilayer lithography techniques to prepare thick traveling-wave electrodes, the problem of irregular cross-sections during the fabrication of thick electrodes has been successfully reduced, improving the stability and controllability of the device. Test results show that the insertion loss of the electro-optic modulator is about 6 dB, the extinction ratio is 36.5 dB, the optical waveguide mode field is 1 μm, the full-band characteristic impedance is 50 Ω, the test bandwidth is 50 GHz, and the half-wave voltage is 1.8 V. Compared with existing optimization schemes, this design not only achieves a large bandwidth and a small half-wave voltage, but also proposes a new fabrication process scheme, optimizing the process and resulting in samples with stable performance.

Capabilities of asymmetric rolling of single-layer and laminated materials made from aluminum and its alloys

Asymmetric rolling of aluminum alloys is one of the methods for improving their mechanical and performance characteristics. Kinematic asymmetry during rolling is achieved by varying the roll speed ratios (V1 /V2). It is believed that when V1 /V2 &gt; 3, the process of asymmetric rolling, by combining significant compression and shear deformations, approximates the processes of severe plastic deformation. It has been found that the majority of studies are based on data obtained within a limited roll speed ratio range, V1 /V2 &lt; 2, in asymmetric rolling. This article examines the effects observed at V1 /V2 = 1÷7.7. The implementation of this condition became possible thanks to a unique scientific facility – the 400 laboratory-industrial asymmetric rolling mill at the Zhilyaev laboratory “Mechanics of Gradient Nanomaterials” at Nosov Magnitogorsk State Technical University Experiments were conducted on asymmetric thin-sheet rolling of aluminum alloys 2024, 5083, and 6061, as well as accumulative roll bonding to produce laminated sheet aluminum composites 5083/2024, 5083/1070, and 6061/5083. The disadvantages of asymmetric rolling compared to symmetric rolling were identified: sample failure was observed at single relative reductions of 37 % for layered sheet aluminum composites (5083/2024) and 40 % for thin-sheet aluminum alloys (6061). The nuances of material preparation for processing were described, including the necessity of cleaning and degreasing the alloy surfaces before bonding into a composite. The rolling temperature regimes were selected, determining cold asymmetric thin-sheet rolling (room temperature processing) and warm asymmetric accumulative roll bonding (heating of the workpieces in the furnace before rolling at 320–350 °C). A reduction in rolling force (by a minimum of 1.3 times), the ability to vary hardness (including an increase by a minimum of 30 %), and technological plasticity with changes in the roll speed ratios within the range of 2 to 7.7 were demonstrated. Options were proposed for reducing the processing cycles of aluminum alloys without compromising the quality of the finished product by reducing the number of rolling passes and annealing steps in the standard process scheme.

A framework for validating efficiency models of thermal separation columns with tray internals

Abstract Tray columns are globally used for distillation processes, and their performance must be optimized to reduce their cost and energy expenditures as well as carbon emissions. A prerequisite to achieve these targets demands a realistic account of the tray performance based on tray efficiency model application. The proof of concept of a Refined Residence Time Distribution (RRTD) model, recently proposed by the authors, is presented. The multifaceted challenges in acquiring hydrodynamic and performance data suitable for model validation are discussed in this work. In that regard, an unprecedented experimental campaign was performed in a representative large-scale air-water tray column simulator based on the application of multiplex flow profiler, new chemical system, and novel data processing schemes. Using these data and additional case studies, the capabilities of the RRTD model were demonstrated. This model successfully accounted for the impacts of varying local liquid flow characteristics and vapor flow maldistributions on the tray efficiency thereby advancing the state of the art of the tray efficiency models. This work particularly aimed at proposing a constructive framework to evaluate tray efficiency models in the future campaigns, and those campaigns will benefit from the learnings of the present work.

An Enhanced Retrieval Scheme for a Large Language Model with a Joint Strategy of Probabilistic Relevance and Semantic Association in the Vertical Domain

Large language model (LLM) processing, with natural language as its core, carries out information retrieval through intelligent Q&amp;A. It has a wide range of application scenarios and is commonly considered a kind of generative AI. However, when LLMs handle generation tasks, the results generated by fundamental LLMs with an insufficient comprehensive performance, specifically in the vertical domain, are often inaccurate due to a poor generalization ability, resulting in the so-called “hallucination” phenomenon. To solve these problems, in this study, an enhanced retrieval scheme for LLM processing was developed, named the BM-RAGAM (BM25 retrieval-augmented generation attention mechanism), by constructing a vectorized knowledge base, utilizing a hybrid joint retrieval strategy of keyword matching through searching and a semantic-enhanced association with an attention mechanism and taking ocean-front- and eddy-related knowledge in oceanography as an example. This scheme realized accurate word-based matching with the BM25 algorithm and text generation through a semantic-enhanced association using RAG, and it was used to construct a vector database of the text knowledge on ocean fronts and eddies. The output was compared and analyzed with the fundamental LLM of Qwen2-72B using the proposed scheme, and an ablation experiment was conducted. The results show that the proposed scheme greatly reduced hallucination generation in the process of text generation, making its outputs more interpretable.

Robust design of mismatched filters in echo truncation for inter‐pulse waveform diversity radar

AbstractRange sidelobe modulation is a primary issue that forbids application of inter‐pulse and intra‐coherent processing interval (coherent processing interval) waveform diversity in moving target indication or moving target detection radars. Previous research has adopted mismatched filters (MMFs) to reconstruct identical compressed outputs based on an ideal model. In practice however, echo truncation is inevitable due to transceiver isolation, significantly degrading the effectiveness of MMFs. In this paper, the design of MMFs under an echo‐truncated model to achieve a robust design is considered. Specifically, the authors extract the dominant scattering profile from the close‐range echo and incorporate it into the MMF design model. To judiciously combine the robust design with the ideal one, a segmented pulse compression scheme is proposed. Numerical results indicate that the proposed robust processing scheme can effectively reduce clutter leakage in both truncated and intact scenarios.

A counter mode and multi-channel based chaotic image encryption algorithm for the internet of things

To deal with the threat of image privacy leakage in the Internet of things, this paper presents a novel batch images encryption algorithm using the counter mode and a multi-channel processing scheme. We employ multi-thread technique combined with an adapter to construct a novel multi-channel processing scheme, which can encrypt four different sized images in one round. Moreover, the counter encryption mode, which can compute round keys from a plaintext related session key, is introduced to decrease the difficulty of session key management when dealing with batch images. The security tests demonstrate the exceptional performance of the proposed algorithm in terms of security, as evidenced by P-values of statistical tests far larger than 0.01, correlation coefficients and entropies of cipher images close to 0 and greater than 7.99. Additionally, the results of NPCR and UACI tests closely approximate the theoretical values 99.6094% and 33.4635%, the proposed algorithm can better resist statistical, exhaustive, differential, or even chosen plaintext attacks. Moreover, due to the novel parallel scheme with a linear time complexity of O(2W+2H), which demonstrates an acceleration of over 300% compared to existing algorithms, it only takes 2.1sto encrypt one hundred images with varying sizes. Therefore, the proposed algorithm succeeds in exceeding existing algorithms in meeting the efficiency and security requirements for encrypting batch images.

Застосування інноваційних інструментів оптимізування бізнес-процесів підприємств в умовах євроінтегрування: виклики і перспективи

The article discusses the challenges and prospects of applying innovative tools for optimizing business processes of enterprises in the context of European integration. The classification of business processes of enteprises, as well as innovations and innovation processes, has been supplemented and systematized. It is stated that despite the war, Ukraine occupies leading positions in the world innovation rankings, and throughout the entire period of the war with Russia it remains one of the economies whose innovative development is carried out at a faster pace than the economic development. This characterization applies to. A scheme for a phased process of reengineering business processes of enterprises on the basis of an innovative approach in the context of European integration is proposed. The main purpose of the article is to study the challenges and prospects in the application of innovative tools for optimizing business processes of domestic enterprises in the context of European integration. Given the current high-risk business environment (military actions, crisis phenomena, etc.) prevailing in Ukraine and the world as a whole, conventional (classical) tools for optimizing business processes simply cease to bring the desired results. Therefore, it is time to improve and develop innovative tools for optimizing business processes of domestic and foreign enterprises. The direction of movement towards innovation management obliges the management of domestic enterprises to focus on a system of business process management in which the search, identification, and implementation of innovations will become a systematic task of each organizational element, structural unit of an enterprise or organization. Given the shortage of working capital in the domestic economy, which also negatively affects the processes of financing innovations, it is advisable to pay attention primarily to the management system of all business processes. Skillful management of the potential of business processes of a domestic organization will allow identifying, creating, and forming competitive advantages, occupying favorable positions in the European market. Moreover, the use of innovative tools for optimizing business processes of enterprises will not always require significant funding, which is important for Ukrainian entrepreneurs in the current environment.

High-fidelity prediction of forming quality for self-piercing riveted joints in aluminum alloy based on machine learning

The effect of rivet length and sheet thickness on the cross-sectional formation and tensile-shear performance of self-piercing riveted joints in AA5754 aluminum alloy was examined through experimental investigation. The influence degree of joining parameters on the forming quality was analyzed. It was revealed that rivet length and sheet thickness are pivotal factors influencing the tensile-shear strength of the joint, culminating in the identification of four optimal riveting process schemes:LCh1Ah2A、LAh1Ah2B、LAh1Ah2B and LBh1Ah2B. A simulation model for self-piercing riveting was established, employing the GISSMO failure model and the modified Mohr-Coulomb (MMC) failure criteria to predict the damage and fracture of the aluminum alloy. A plethora of high-quality datasets depicting the cross-sections of the joints were derived from simulation analysis. Subsequently, the structure and hyperparameter determination method of traditional neural network prediction models were elucidated. By amalgamating the Aquila Optimization (AO) algorithm with the African Vultures Optimization Algorithm (AVOA), a hybrid optimization algorithm model known as MIC_AOAVOA was developed. This model effectively harnesses the strengths of various algorithms to augment search efficiency and optimization capabilities. Strategies for population initialization and adaptive weight adjustments were incorporated to enhance the algorithm's convergence velocity and the quality of solutions. The cauchy opposition-based learning (COBL) and fitness-distance balance (FDB) strategy further refined the composite algorithm, bolstering its global search capabilities and population diversity. Comparative analyses were performed with single algorithm models and traditional BP neural network models, with an in-depth examination of the MIC_AOAVOA_BP model's prediction outcomes. Comprehensive evaluations utilizing error statistics and composite evaluation indicators demonstrated that the model consistently achieved mean absolute percentage error (MAPE) values below 10%, correlation coefficients (R²) exceeding 0.98, and stable mean squared error (MSE) values around 0.0002 across the prediction of three metrics. These results underscore the model's high precision and stability. Consequently, the proposed enhanced model offers a solution that is more stable, accurate, and robust for the prediction of forming quality in self-piercing riveted joints within engineering applications.

COMPREHENSIVE PROCESSING OF MINERAL RAW MATERIALS FROM COAL MINE WASTE DUMPS: PROPERTIES, VALUABLE PRODUCTS, AND RESOURCE ACCOUNTING SYSTEM

Objective. Experimental studies on the chemical and mineralogical composition of coal mine spoil heaps and the potential for processing them to obtain valuable mineral-raw materials. Development of an analytical tool for accounting and forecasting the resource potential of coal mine spoil heaps. Methods. To achieve the objective, X-ray fluorescence analysis, X-ray phase analysis, granulometric analysis, laboratory studies of mineral raw material enrichment using sieve fractionation and gravity separation, as well as analytical studies of spoil heap resource accounting were employed. Results. The chemical, mineralogical, and granulometric composition of spoil heaps from a coal mine in the Western Donbas region was investigated, enabling the identification of a processing strategy to obtain a range of products: coal concentrate, crushed stone mixture, sand fraction, and clay fraction. It was established that gravity separation of coarse fractions in a heavy medium created with clay slurry demonstrated high separation efficiency, yielding a significant low-ash coal fraction and a high-ash rock fraction. In contrast, spiral separator processing demonstrated significantly lower low-ash coal recovery. A technological processing scheme for spoil heaps to obtain a complex of valuable products was developed, and forecast indicators were evaluated. A methodological system for accounting the resource potential of a coal mine waste dump has been proposed in the form of an informational database. This system allows for tracking changes in the dumps structure, identifying zones of attractive resource accumulation for economic efficiency of processing operations in the long term. Scientific novelty. The use of clay suspension from washed spoil heaps to create a heavy medium for the beneficiation of coarse fractions allows for the recovery of up to 95% coal concentrate with 11% ash content, contributing to resource conservation. Practical significance. A technological scheme for obtaining a range of valuable products for the energy and construction industries has been developed. An analytical tool for resource potential accounting and calendar planning for spoil heap formation has been created, enabling operational identification of resource concentration zones within spoil heaps and their sections. Keywords: coal mine, spoil heap, valuable resource, chemical composition, mineralogical composition, granulometric composition, gravity separation, resource accounting.

Determination of technological schemes for extraction of gold from ores of the Gyzylbulag deposit and their practical importance

This article is devoted to the study and determination of optimal process flow charts for gold extraction from the Gyzylbulag deposit ores, as well as to the analysis of their practical significance in the context of modern mining production. In the context of growing demand for gold and resource limitations, it is important to develop effective methods that not only increase the extraction rate, but also minimize the environmental impact and economic costs.The article considers the features of the ore composition of the Gyzylbulag deposit, their mineral and chemical composition, which play a key role in choosing the appropriate extraction technology. The authors analyze in detail the efficiency of traditional methods, such as gravity concentration, for processing these ores, considering each of them in terms of extraction rate. Particular attention is paid to the integration of various methods into a single process flow chart, which allows to increase the overall gold extraction rate and reduce losses of the precious metal at all stages of processing. The practical significance of the results lies in the proposal of processing schemes that can be applied not only at the Gyzylbulag deposit, but also in other regions with similar geological conditions. These schemes are aimed at optimizing gold extraction while reducing production costs and environmental impact, which meets the objectives of sustainable development in the mining industry. The article is addressed to specialists in the field of mineral processing, researchers and practitioners involved in the development of effective methods for processing gold-bearing ores. Keywords: native gold; gravity; gold dimensions; flotation.

A lightweight approach to real-time speaker diarization: from audio toward audio-visual data streams

This manuscript deals with the task of real-time speaker diarization (SD) for stream-wise data processing. Therefore, in contrast to most of the existing papers, it considers not only the accuracy but also the computational demands of individual investigated methods. We first propose a new lightweight scheme allowing us to perform speaker diarization of streamed audio data. Our approach utilizes a modified residual network with squeeze-and-excitation blocks (SE-ResNet-34) to extract speaker embeddings in an optimized way using cached buffers. These embeddings are subsequently used for voice activity detection (VAD) and block-online k-means clustering with a look-ahead mechanism. The described scheme yields results similar to the reference offline system while operating solely on a CPU with a low real-time factor (RTF) below 0.1 and a constant latency of around 5.5 s. In the next part of the work, our research moves toward much more demanding and complex real-time processing of audio-visual data streams. For this purpose, we extend the above-mentioned scheme for audio data processing by adding an audio-video module. This module utilizes SyncNet combined with visual embeddings for identity tracking. Our resulting multi-modal SD framework then combines the outputs from audio and audio-video modules by using a new overlap-based fusion strategy. It yields diarization error rates that are competitive with the existing state-of-the-art offline audio-visual methods while allowing us to process various audio-video streams, e.g., from Internet or TV broadcasts, in real-time using GPU and with the same latency as for audio data processing.

Flatness constraints in the estimation of GNSS satellite antenna phase center offsets and variations

Accurate information on satellite antenna phase center offsets (PCOs) and phase variations (PVs) is indispensable for high-precision geodetic applications. In the absence of consistent pre-flight calibrations, satellite antenna PCOs and PVs of global navigation satellite systems are commonly estimated based on observations from a global network, constraining the scale to a given reference frame. As part of this estimation, flatness and zero-mean conditions need to be applied to unambiguously separate PCOs, PVs, and constant phase ambiguities. Within this study, we analytically investigate the impact of different boresight-angle-dependent weighting functions for PV minimization, and we compare antenna models generated with different observation-based weighting schemes with those based on uniform weighting. For the case of the GPS IIR/-M and III satellites, systematic differences of 10 mm in the PVs and 65 cm in the corresponding PCOs are identified. In addition, new antenna models for the different blocks of BeiDou-3 satellites in medium Earth orbit are derived using different processing schemes. As a drawback of traditional approaches estimating PCOs and PVs consecutively in distinct steps, it is shown that different, albeit self-consistent, PCO/PV pairs may result depending on whether PCOs or PVs are estimated first. This apparent discrepancy can be attributed to potentially inconsistent weighting functions in the individual processing steps. Use of a single-step process is therefore proposed, in which a dedicated constraint for PCO-PV separation is applied in the solution of the normal equations. Finally, the impact of neglecting phase patterns in precise point positioning applications is investigated. In addition to an overall increase of the position scatter, the occurrence of systematic height biases is illustrated. While observation-based weighting in the pattern estimation can help to avoid such biases, the possible benefit depends critically on the specific elevation-dependent weighting applied in the user’s positioning model. As such, the practical advantage of such antenna models would remain limited, and uniform weighting is recommended as a lean and transparent approach for the pattern estimation of satellite antenna models from observations.