Relevant Process Parameters Research Articles

A Novel Approach for Non-Invasive Continuous In-Line Control of Perfusion Cell Cultivations by Raman Spectroscopy.

Continuous manufacturing is becoming more important in the biopharmaceutical industry. This processing strategy is favorable, as it is more efficient, flexible, and has the potential to produce higher and more consistent product quality. At the same time, it faces some challenges, especially in cell culture. As a steady state has to be maintained over a prolonged time, it is unavoidable to implement advanced process analytical technologies to control the relevant process parameters in a fast and precise manner. One such analytical technology is Raman spectroscopy, which has proven its advantages for process monitoring and control mostly in (fed-) batch cultivations. In this study, an in-line flow cell for Raman spectroscopy is included in the cell-free harvest stream of a perfusion process. Quantitative models for glucose and lactate were generated based on five cultivations originating from varying bioreactor scales. After successfully validating the glucose model (Root Mean Square Error of Prediction (RMSEP) of ∼0.2 g/L), it was employed for control of an external glucose feed in cultivation with a glucose-free perfusion medium. The generated model was successfully applied to perform process control at 4 g/L and 1.5 g/L glucose over several days, respectively, with variability of ±0.4 g/L. The results demonstrate the high potential of Raman spectroscopy for advanced process monitoring and control of a perfusion process with a bioreactor and scale-independent measurement method.

Characterization of critical roughness indicators by digital image processing to predict contact angles on hydrophobic surfaces

This study innovatively applies digital imaging techniques to quantify the irregular nanoscale roughness of the surfaces of plasma-etched, hydrophobic polymer films. The main objective is to verify meaningful roughness indicators that predict the surface wettability by quantifying the surface nanostructures in a simple way. Five texture parameters were extracted from SEM images by box-counting and a gray-level co-occurrence matrix (GLCM) algorithm, which together provide 2D spatial information of roughness features. Also, five parameters related to height and aspect ratio profiles of roughness were obtained using AFM analysis. The 2D texture parameters and AFM roughness profiles were statistically correlated, and either set could be used to predict surface wettability with high explanatory power. The prediction was most powerful when variables of fractal dimension, arithmetic mean height Ra of roughness features in the assessed area, and skewness Rsk of height in the assessed area were used in the regression model. This result indicates that both the height profile and 2D spatial distribution of roughness features strongly affect surface wettability. The correlation of gray levels with the neighboring pixels was statistically correlated with Ra and Rsk, and a simple predictive model was developed using fractal dimension and correlation. This research is significant in that it explored a novel analytical approach in assessing the roughness characteristics to predict surface wettability. Ultimately, we anticipate that the developed approach can be applied in designing relevant process parameters to control the wetting properties.

Experimental Analysis of Ductile Cutting Regime in Face Milling of Sintered Silicon Carbide.

In this study, sintered silicon carbide is machined on a high-precision milling machine with a high-speed spindle, closed-loop linear drives and friction-free micro gap hydrostatics. A series of experiments was undertaken varying the relevant process parameters such as feedrate, cutting speed and chip thickness. For this, the milled surfaces are characterized in a process via an acoustic emission sensor. The milled surfaces were analyzed via confocal laser scanning microscopy and the ISO 25178 areal surface quality parameters such as Sa, Sq and Smr are determined. Moreover, scanning electron microscopy was used to qualitatively characterize the surfaces, but also to identify sub-surface damages such as grooves, breakouts and pitting. Raman laser spectroscopy is used to identify possible amorphization and changes to crystal structure. We used grazing incidence XRD to analyze the crystallographic structure and scanning acoustic microscopy to analyze sub-surface damages. A polycrystalline diamond tool was able to produce superior surfaces compared to diamond grinding with an areal surface roughness Sa of below 100 nm in a very competitive time frame. The finished surface exhibits a high gloss and reflectance. It can be seen that chip thickness and cutting speed have a major influence on the resulting surface quality. The undamaged surface in combination with a small median chip thickness is indicative of a ductile cutting regime.

Bridging the Gap between Physical and Circuit Analysis for Variability-Aware Microwave Design: Modeling Approaches

Process-induced variability is a growing concern in the design of analog circuits, and in particular for monolithic microwave integrated circuits (MMICs) targeting the 5G and 6G communication systems. The RF and microwave (MW) technologies developed for the deployment of these communication systems exploit devices whose dimension is now well below 100 nm, featuring an increasing variability due to the fabrication process tolerances and the inherent statistical behavior of matter at the nanoscale. In this scenario, variability analysis must be incorporated into circuit design and optimization, with ad hoc models retaining a direct link to the fabrication process and addressing typical MMIC nonlinear applications like power amplification and frequency mixing. This paper presents a flexible procedure to extract black-box models from accurate physics-based simulations, namely TCAD analysis of the active devices and EM simulations for the passive structures, incorporating the dependence on the most relevant fabrication process parameters. We discuss several approaches to extract these models and compare them to highlight their features, both in terms of accuracy and of ease of extraction. We detail how these models can be implemented into EDA tools typically used for RF and MMIC design, allowing for fast and accurate statistical and yield analysis. We demonstrate the proposed approaches extracting the black-box models for the building blocks of a power amplifier in a GaAs technology for X-band applications.

Numerical Simulation of Impregnation Process of Reactive Injection Pultrusion for Glass Fiber/PA6 Composites.

Pultrusion of thermoplastic composites has been the hotspot of manufacturing high-performance thermoplastic composites in recent years. The optimization of process parameters in the pultrusion usually needed repeated attempts, which wasted lots of manpower and material resources. A numerical simulation method can accelerate the optimization of process parameters. In this work, the impregnation process of reactive injection pultrusion for glass fiber reinforced nylon 6 (GF/PA6) composites was modeled and numerically simulated by a finite element/controlled volume (Fe/CV) method. Based on Darcy’s law, the impregnation process can be regarded as the two-phase flow (liquid resin and air) in porous media (undirectional glass fibers). The distribution of resin flow during the impregnation was explored. The effects of pulling rate and injection pressure on the impregnation time and resin reflux distance were analyzed, and the appropriate range of relevant process parameters was determined. The results showed that increasing the pulling rate can significantly control the reflux distance of resin in the impregnation mold and shorten the impregnation time, but too high a pulling rate would increase the impregnation time. Increasing the injection pressure can greatly shorten the resin impregnation time, but it would significantly increase the resin reflux distance. This work can effectively guide the subsequent optimization of process parameters of reactive injection pultrusion for GF/PA6 composites.

Conversion of paper and xylan into laser-induced graphene for environmentally friendly sensors

Laser-induced graphene (LIG) is a foam-like porous material consisting of few-layer graphene obtained by laser irradiation of a wide range of carbon-containing substrates. Among these, the ability to synthesize LIG from paper and other cellulose-related materials is particularly exciting, as it opens the door to a wide assortment of potential applications in the form of low-cost, flexible, and biodegradable devices. Here, the synthesis of this material, dubbed paper-LIG, on different types of filter papers and xylan biopolymer is discussed. In particular, we report the formation of paper-LIG by single-step irradiation, providing an improvement over the conventional multiple lasing approach and giving an explanation of the conditions that allow this simplified synthesis. All the relevant process parameters are covered, assessing their effect on the resulting electrical properties, structure, and morphology. Additionally, we demonstrate the application of LIG obtained from xylan, an abundant and often underutilized biopolymer, for temperature sensing. These results provide a better understanding of the conditions required for the synthesis of highly conductive LIG from paper and related materials, paving the way for its application, with reduced cost and low environmental impact, in fields ranging from biomonitoring to consumer electronics.

Dairy foods and novel thermal and non-thermal processing: A bibliometric analysis

Processing dairy products by conventional methods, such as pasteurization, can induce chemical reactions and physicochemical alterations, compromising nutritional and sensory quality. Thus, there is a growing worldwide demand for studies related to emerging processing technologies. This study aims to describe trends related to dairy processing through the application of emerging technologies. In this sense, articles published in this area of research in the last decade were retrieved from scientific databases, and their findings were analyzed through a bibliometric study. Reviews and original articles expressed 40 and 60% of publications, respectively, and novel thermal and non-thermal processing studies showed multidisciplinary approaches. Ohmic heating and microwave heating were the most discussed novel thermal technologies in the processing of dairy products. Among non-thermal technologies, there was a more significant trend in studies on ultrasound, high hydrostatic pressure, and pulsed electric fields. The impact of the application of novel food technologies concerning quality, safety, and energy efficiency compared to traditional methods and the influence of critical operating conditions for process control were the most studied areas. Furthermore, the impact of novel non-thermal food processing technologies from the consumer's point of view is emerging. These results can be used for future innovations by the dairy industry. Industrial relevanceDairy foods are important food matrices considering a nutritional and functional point of view and recommended in a regular diet for consumers of all ages. Therefore, a bibliometric analysis was performed considering dairy foods and novel thermal and non-thermal processing. A higher number of publications considering non-thermal technologies was reported, highlighting the need for food processing without applying high temperatures. High hydrostatic pressure, ultrasonication, and pulsed electric fields were the most discussed non-thermal technologies. At the same time, ohmic heating and microwave heating were the most reported thermal technologies. Furthermore, the most used applications, critical process parameters, and food properties to be evaluated are highlighted and discussed. The results could be used as a basis for the dairy processors to implement non-conventional technologies as an option at their production lines, as they demonstrate the most relevant process parameters and food characteristics to be evaluated, helping them to choose the parameters with higher impact and firstly consider them. Furthermore, consumers studies are important and should evaluate consumer perception about the products and the perceived benefits and risks of the novel technologies.

Evaluating Machine Learning-Based Classification Approaches: A New Method for Comparing Classifiers Applied to Human Driver Prediction Intentions

In this research, a new performance assessment based on the Probability of Detection (POD) reliability measure is developed integrating and discussing the effect of further parameters on classification results and therefore establishing a new connection between relevant process parameters and the related classifier evaluation. To illustrate the introduced approach, machine learning-based recognition of complex driving situations for human drivers is interpreted. Based on sensor signals and a complex driving scenario, related dynamical changes are classified and compared using the POD approach. Based on the POD-related evaluation, different machine learning approaches can be clearly distinguished with respect to their ability to predict the correct driver behavior as a function of time prior to the event itself. The introduced approach allows a very detailed comparison of classifiers related to the effects of parameters affecting the processes to be classified. In addition to recently published results on this novel approach, an extension of the POD approach by considering false positives and varying decision threshold in the comparison process is proposed. Generalization of the introduced approach for binary and continuous data is presented.

Demand side management in the cooling supply of brewing processes

Due to the increasing supply of volatile renewable energy, demand side management on the consumer side by temporally shifting electrical power demand is needed. Industrial cooling systems in particular offer great energy-flexibility potential since cooling energy is mostly electrically generated and almost all cooling systems already have thermal energy storage. Compared to other industrial sectors, the share of cooling energy demand in the food and beverage industry is particularly high. Especially in breweries, where large beer tanks require cooling, promising energy-flexibility potentials are expected. In order to quantify the technical energy-flexibility potential in the brewing process, relevant thermal energy consumers and process parameters are identified in a first step. Then, various energy-flexibility measures as well as their possible implementation barriers are identified and compared in terms of their technical energy-flexibility potential. The potential of the most promising energy-flexibility measure is then examined in detail by taking the cooling power demand and operating duration into consideration. The analysis shows that the focused beer cooling process in the storage tanks, accounts for around 45 % of the total cooling demand and can be shifted in time by up to 96 hours. Finally, the economic energy-flexibility potentials are determined. Taking advantage of the lowest electricity prices within one week, the results of a retrospectively optimized operation strategy show that energy cost savings of approx. 9 % can be achieved, without taking possibly increasing energy price fluctuations into account.

Virtual monitoring system for a robotic manufacturing station in intelligent manufacturing based on Unity 3D and ROS

In the present work, a digital twin system has been developed for a small-scale robotic manufacturing station, typically for realization of intelligent additive manufacturing. This system consists of: (1) virtual model and space construction (in Unity 3D), (2) operating simulation of robot movement (in Robot Operating System, ROS) and the pick-and-place action, (3) data communication and transmission between the physical manufacturing station and the virtual model, and (4) real-time dynamic display of the operating status of the robot arm (display of joint angle variations with time), etc. By establishing dual-direction data communication between the physical equipment and virtual environment, real-time data drives the movement of the virtual model which enables remote, real-time visualization and monitoring of the entire working process of the physical additive manufacturing station. It is expected the digital twin model can be extended to more sophisticated additive manufacturing applications such as 3D printing for not only monitoring but also control and optimization of the relevant process parameters.

A systematic techno-economic approach to decide between continuous and batch operation modes for injectable manufacturing

A comprehensive approach is proposed to systematically determine the optimal mode of operation between continuous and batch injectable manufacturing considering product and market conditions. At the core of this approach are two integrated complete mathematical modules for discrete and continuous injectable manufacturing, which are supplemented with an economic evaluation module that can then be used to explore the impact of all relevant process parameters (e.g., lot-size, number of operators, solubility, product demand, raw material costs). When the developed approach was applied to two case studies, it was found that batch production was preferred at low to moderate solution (raw material) costs. In contrast, at higher solution costs, the preference for batch and continuous production processes changed back and forth as the annual product demand changed. The study also found that continuous production processes became increasingly preferred at medium to large final dosage volumes and a competitive alternative even at moderate solution costs. From a decision-making point of view, batch injectable manufacturing will be preferred over the novel continuous manufacturing technology unless there is a significant economic incentive to overcome the perceived technology risk. The proposed approach is intended as a decision-support tool for pharmaceutical process engineers.

Fabrication of Sb2S3 Thin Films by Low-Temperature Plasma-Sulfurizing Metallic Sb Layers

In this work, antimony sulfide (Sb2S3) thin films were prepared by low-temperature plasma (about 190 °C) sulfidation of antimony metal layers, and the sulfidation mechanism was investigated. Solid sulfur powder was used as the sulfur source, and the reaction process did not exhaust toxic gases. The relevant process parameters, including the reaction pressure, radio frequency (RF) power, and sulfurization time, were also optimized. Under conditions of an RF power of 180 W (approximately 190 °C), a curing time of 60 min, and a working pressure of 30 Pa, we prepared a smooth, pinhole-free, (211) preferentially oriented Sb2S3 thin film. The film obtained good photocurrent response and a suitable band gap of 1.74 eV. The properties and structures of the Sb2S3 thin film were characterized by optical emission spectroscopy, high-resolution scanning electron microscopy, energy-dispersive spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and UV and Raman spectroscopies.

Atmospheric-Pressure Plasma-Enhanced Spatial ALD of SiO2 Studied by Gas-Phase Infrared and Optical Emission Spectroscopy.

An atmospheric-pressure plasma-enhanced spatial atomic layer deposition (PE-s-ALD) process for SiO2 using bisdiethylaminosilane (BDEAS, SiH2[NEt2]2) and O2 plasma is reported along with an investigation of its underlying growth mechanism. Within the temperature range of 100–250 °C, the process demonstrates self-limiting growth with a growth per cycle (GPC) between 0.12 and 0.14 nm and SiO2 films exhibiting material properties on par with those reported for low-pressure PEALD. Gas-phase infrared spectroscopy on the reactant exhaust gases and optical emission spectroscopy (OES) on the plasma region are used to identify the species that are involved in the ALD process. Based on the identified species, we propose a reaction mechanism where BDEAS molecules adsorb on −OH surface sites through the exchange of one of the amine ligands upon desorption of diethylamine (DEA). The remaining amine ligand is removed through combustion reactions activated by the O2 plasma species leading to the release of H2O, CO2, and CO in addition to products such as N2O, NO2, and CH-containing species. These volatile species can undergo further gas-phase reactions in the plasma as indicated by the observation of OH*, CN*, and NH* excited fragments in OES. Furthermore, the infrared analysis of the precursor exhaust gas indicated the release of CO2 during precursor adsorption. Moreover, this analysis has allowed the quantification of the precursor depletion yielding values between 10 and 50% depending on the processing parameters. Besides providing insights into the chemistry of atmospheric-pressure PE-s-ALD of SiO2, our results demonstrate that infrared spectroscopy performed on exhaust gases is a valuable approach to quantify relevant process parameters, which can ultimately help evaluate and improve process performance.

Comparative Study of Optimization Models for Evaluation of EDM Process Parameters on Ti-6Al-4V

In this paper, different optimization techniques such as multi-objective optimization based on ratio analysis, the -nD angle, information divergence and multi-angle optimization methods were considered for comparative study in order to check their variation in results. These four techniques were considered asthese techniques were made from completely different methodologies, such as MOORA, is one of the latest multi-criteria decision-making methods that cover the weakness of other older methods and the latter three (-nD angle, Information Divergence and MAOT) methods are based on trigonometric methodologies. These optimization techniques were carried out using the effect of EDM process parameters viz., material removal rate and surface roughness as output parameters and current, pulse on time, pulse off time and different electrode materials as input process parameters. These parameters were obtained from the experiments modelled according to the Taguchi experimental design procedure in which the L18 orthogonal array was used to perform experimentation with the material of titanium alloy (Ti-6Al-4V). All the optimization techniques have obtained similar results among which -nD angle and Information Divergence technique tend to be easier and understandable for any similar application. The latter two methods avoid complexity and can be used for optimizing any relevant manufacturing process parameters such as the parameters of EDM, additive manufacturing, etc.

En route towards a comprehensive dimensionless representation of precipitation processes

Precipitation of (nano-) particles is a unit operation for the preparation of suspensions. Despite its widespread use, no general understanding is established of how different operating conditions, such as mixing rates or reactant concentrations, affect the precipitation outcome (e.g. the particle size distribution). We obtain overarching relations between the operating conditions and the precipitation outcome by deriving dimensionless numbers governing the precipitation process. In particular, we consider the interdiffusion of two chemical compounds reacting to a sparingly soluble salt by coupling the reaction–diffusion equations for the solute concentrations, to a population balance equation for the evolution of the dispersed phase including nucleation and growth. We vary the relevant process parameters for different chemical systems and uncover three dimensionless numbers, which fully determine the precipitation outcome under the assumption of fast chemical reactions. Two of these dimensionless numbers are Damköhler numbers, which put the relevant mixing kinetics in relation to the relevant time scales for the nucleation and the growth kinetics, respectively. The third dimensionless number corresponds to a dimensionless solid concentration. Simple functional expressions for the Damköhler numbers valid for any chemical system allow estimating whether the precipitation product is controlled by mixing, or solely by the kinetics of solid formation. In the mixing-controlled regime, scaling laws relate the mean particle sizes and the number of particles to the Damköhler number.

Predicting the concentration of hazardous phenolic compounds in refinery wastewater-a multivariate data analysis approach.

The present study focused on the methodology for identification of the wastewater stream that presents the highest phenolic impact at a large oil refinery. As a case-study, the oil refinery, Petrogal S.A., in Sines, Portugal, was selected. Firstly, stripped sour water from the cracking complex was identified as the most relevant wastewater stream concerning phenolic emission. Secondly, multivariate data analysis was used, through projection to latent structures (PLS) regression, to find existing correlations between process parameters and phenols content in stripped sour water. The models developed allowed the prediction of phenols concentration with predictive errors down to 20.16 mg/L (corresponding to 8.2% average error), depending on the complexity of the correlation used, and R2 values as high as 0.85. Models were based in input parameters related to fluid catalytic crackers (FCC) feedstock quality, crudemix and steam injected in the catalyst stripper. The studied data analysis approach showed to be useful as a tool to predict the phenolic content in stripped sour water. Such prediction would help improve the wastewater management system, especially the units responsible for phenol degradation. The methodology shown in this work can be used in other refineries containing catalytic cracking complexes, providing a tool which allows the online prediction of phenols in stripped sour water and the identification of the most relevant process parameters. An optimised system at any refinery leads to an improvement in the wastewater quality and costs associated with pollutant discharge; thus, the development of monitoring online tools, as proposed in this work, is essential.

Simulation of a Natural Gas Steam Reforming Plant for Hydrogen Production Optimization

AbstractA simulation study to predict and optimize a natural gas steam reforming plant in Suez was performed. The developed model was used to generate performance data that would map the relation between atthe operating variables and hydrogen productivity. A single objective optimization problem was then formulated to maximize hydrogen production in the subunits: hydrocarbon prereformer, steam methane reformer, and the medium‐temperature water‐gas‐shift reactor. The process temperature and pressure for the three subunits, total plant flow rate and total superheated steam flow rate were selected as decision variables. A good agreement was found between simulation results and plant data at steady‐state conditions in terms of hydrogen yield and chemical composition. The developed mathematical model for hydrogen productivity correctly predicts the effect of relevant process parameters.

Coupling of attrition and accelerated carbonation for CO2 sequestration in recycled concrete aggregates

An accelerated carbonation method was applied to improve the ability of recycled aggregates to store CO2 through the rolling carbonation process; moreover, recycled concrete aggregates’ physicochemical properties to produce secondary aggregates for civil engineering applications were explored and evaluated. The carbonation tests were carried out in a laboratory-scale air-proof rotating drum equipped with a CO2 feeding system. Performing carbonation in a rotating drum promotes selective attrition of concrete aggregates. The process accelerates the carbonation by removing the carbonated zones on the surface of coarse aggregates and then facilitating the access of CO2 to un-carbonated zones. The tests were performed at normal atmospheric conditions, with a partial pressure of CO2 kept at about 0.75 bar. The optimal initial moisture of the concrete aggregate was found. The main tests were conducted on 10/20 mm aggregate, but it was shown that the carbonation process was 20% faster for smaller size aggregate (5/8 mm). The rolling speed, initial moisture content, and mass amount played an important role in CO2 sequestration. The most efficient rotating carbonation condition was reached at 100 rpm, aggregate moisture around 8%, and 500 g of aggregates in a 5 L reactor at any partial pressure. The dynamic process highly increased the carbonation kinetics compared with static carbonation of the same duration. The CO2 sequestered was up 80 mg/g of aggregate, estimated to be around 60% of the potential of CO2 capture of the tested recycled concrete aggregate. The number of revolutions appears to be a relevant process parameter concerning the carbonation kinetics. The carbonation duration acts in a complementary way as in a static process. Some physical properties of the recycled concrete aggregate were determined. As a result, the carbonation process reduced water absorption and enhanced the density of the aggregates.

Deep eutectic solvent functionalized rice husk ash for effective adsorption of ofloxacin from aqueous environment

Deep eutectic solvents (DESs) have achieved the rising attention of the scientific community because of their distinctive physicochemical properties and variety of applications. Herein, DES composed of choline chloride as hydrogen bond acceptor (HBA) and glycolic acid as hydrogen bond donor (HBD) was synthesized. Next, the prepared DES was examined as a functionalization agent for rice husk ash (RHA) to form a novel adsorbent (DES–RHA). To ensure the formation of DES and to recognize the modifications occurred due to the functionalization process, a comprehensive characterization study was performed using 1HNMR, FTIR spectroscopy, TGA, XRD, FESEM, HR-TEM and BET surface area. Potential of the prepared DES–RHA was investigated for the uptake of ofloxacin (OFL) from an aqueous environment. The impact of relevant process parameters was evaluated under optimum conditions, and the data were examined applying various kinetic and isotherm models. As per the regression findings, adsorption kinetics data were well described by pseudo-second-order model, and the isotherm data were in good agreement with Langmuir, Temkin, RP and Freundlich isotherm models. Further, the adsorption procedure was endothermic and spontaneous. The high regeneration and adsorption capacity of DES–RHA than untreated RHA adds a promising approach to eliminate emerging pollutants present in effluent sites.

Data-driven analysis of cold-formed pin structure characteristics in the context of versatile joining processes

Due to increasingly strict emission targets and regulatory requirements, especially for companies in the transport industry, the demand for multi-material-systems is continuously rising in order to lower energy consumption. In this context, mechanical joining processes offer an environmentally friendly and flexible alternative to established joining methods, especially in the field of lightweight design. For example, cold-formed cylindrical pin structures show high potentials in joining multi-material-systems without auxiliary elements. The pin structures are joined either by pressing them directly into the joining partner or by caulking with a pre-punched part. However, to evaluate the strength of the joint and to ensure the joining reliability for versatile processes, such as changing joining partners or batch variations, engineering designers currently have only limited design principles available compared to thermal joining processes. Consequently, the design of an optimal pin joint requires cost- and time-intensive experimental investigations and adjustments to design or process parameters. As a solution, data-driven methods offer procedures for structuring data and identifying dependencies between varying process parameters and resulting pin structure characteristics. Motivated by this, the paper presents an approach for the data-driven analysis of cold-formed pin structures and offers a deeper understanding of how versatile processes affect the pin characteristics. Therefore, the application of an intelligent design of experiment in combination with several machine learning methods enable the setup of a best-fitting meta-model. Resulting, the determination of a mathematical model provides the opportunity to accurately estimate the pin height considering only relevant geometrical and process parameters with a prediction quality of 95 %.