Prediction Of Process Performance Research Articles

Liquid Distribution in a Semi-Industrial Packed Column - Experimental and Theory

The scientific interest in the efficiency of packed bed columns is part of the world-wide pursuit of sustainability of processes. The maldistribution of the phases in the apparatus reduces the efficiency and makes difficult the prediction of process performance and scaling up. In the present work the operation of liquid distribution devices and high performance packings are investigated addressing the reasons for hydrodynamic non-uniformity of the liquid phase, including the formation and development of wall flow. Data are obtained from semi-industrial size experimental studies and mathematical modeling of the liquid flow through a layer of random Raschig Super-Ring packing. The effect of measures for ensuring uniform initial liquid distribution in the column apparatus is evaluated and the parameters in the mathematical model are identified.

Mesophilic anaerobic co-digestion of the organic fraction of municipal solid waste with the liquid fraction from hydrothermal carbonization of sewage sludge

In the present study, the influence of substrate pre-treatment (grinding and sieving) on batch anaerobic digestion of the organic fraction of municipal solid waste (OFMSW) was first assessed, then followed by co-digestion experiments with the liquid fraction from hydrothermal carbonization (LFHTC) of dewatered sewage sludge (DSS). The methane yield of batch anaerobic digestion after grinding and sieving (20 mm diameter) the OFMSW was considerably higher (453 mL CH4 STP g−1 VSadded) than that of untreated OFMSW (285 mL CH4 STP g−1 VSadded). The modified Gompertz model adequately predicted process performance. The maximum methane production rate, Rm, for ground and sieved OFMSW was 2.4 times higher than that of untreated OFMSW. The anaerobic co-digestion of different mixtures of OFMSW and LFHTC of DSS did not increase the methane yield above that of the anaerobic digestion of OFMSW alone, and no synergistic effects were observed. However, the co-digestion of both wastes at a ratio of 75% OFMSW-25% LFHTC provides a practical waste management option. The experimental results were adequately fitted to a first-order kinetic model showing a kinetic constant virtually independent of the percentage of LFHTC (0.52–0.56 d−1) and decreasing slightly for 100% LFHTC (0.44 d−1).

Review of the Scientific Understanding of Radioactive Waste at the U.S. DOE Hanford Site.

This Critical Review reviews the origin and chemical and rheological complexity of radioactive waste at the U.S. Department of Energy Hanford Site. The waste, stored in underground tanks, was generated via three distinct processes over decades of plutonium extraction operations. Although close records were kept of original waste disposition, tank-to-tank transfers and conditions that impede equilibrium complicate our understanding of the chemistry, phase composition, and rheology of the waste. Tank waste slurries comprise particles and aggregates from nano to micro scales, with varying densities, morphologies, heterogeneous compositions, and complicated responses to flow regimes and process conditions. Further, remnant or changing radiation fields may affect the stability and rheology of the waste. These conditions pose challenges for transport through conduits or pipes to treatment plants for vitrification. Additionally, recalcitrant boehmite degrades glass quality and the high aluminum content must be reduced prior to vitrification for the manufacture of waste glass of acceptable durability. However, caustic leaching indicates that boehmite dissolves much more slowly than predicted given surface normalized rates. Existing empirical models based on ex situ experiments and observations generally only describe material balances and have not effectively predicted process performance. Recent advances in the areas of in situ microscopy, aberration-corrected transmission electron microscopy, theoretical modeling across scales, and experimental methods for probing the physics and chemistry at mineral-fluid and mineral-mineral interfaces are being implemented to build robustly predictive physics-based models.

Intelligent Manufacturing: TCAD-Assisted Adaptive Weighting Neural Networks

Using machine intelligence on device and process performance prediction is an emerging methodology in the IC industry. While semiconductor technology computer-aided design (TCAD) has been researched and developed for over 30 years, it should contribute to or be used in conjunction with machine learning algorithms in solution finding procedure. Here, we propose an adaptive weighting neural network (AWNN) model that combines the advantages of statistical the machine learning model and the physical TCAD model. Using aspect ratio dependent etching as an example, our proposed AWNN outperforms conventional artificial neural network in terms of mean square errors in the test set where 5-10 times reduction is observed. The effectiveness of the TCAD AWNN model can be especially effective in the case of sampling over a vast sample space since the under-sampling problem can be compensated by the TCAD model. The large and nearly unbounded sample space is very common in IC technology, where cascaded and repeated process steps exist (~150 process steps and ~20 masks for 90-nm CMOS process).

A multiscale model of influenza A virus replication covering highly different infection conditions in cell cultures

The multiplicity of infection (MOI) is an important parameter for influenza A virus replication in animal cell culture. Depending on the respective question addressed in research or vaccine production, a range comprising several orders of magnitude is typically covered. Mathematical models, however, have paid little attention to the impact of MOI on virus replication dynamics so far. To overcome this limitation, we implemented kinetics that consider the time point of cell infection in an existing model of influenza A virus replication. We find that the extended model closely captures infection dynamics for MOI conditions ranging from 10-4 to more than one infectious virion per cell. Additionally, this model allows the reproduction of both the concentration of infectious virions and the total number of virus particles. Therefore, the extended model is well suited to predict process performance and to optimize influenza A virus production in cell cultures.

Sustaining an efficient and effective CHO cell line development platform by incorporation of 24-deep well plate screening and multivariate analysis.

Efficient and effective cell line screening is paramount toward a successful biomanufacturing program. Here we describe the implementation of 24-deep well plate (24-DWP) screening of CHO lines as part of the cell line development platform at AbbVie. Incorporation of this approach accelerated the identification of the best candidate lines for process development. In an effort to quantify and predict process performance comparability, we compared cell culture performance in and in shake flasks, for a panel of Chinese Hamster Ovary cell lines expressing a monoclonal antibody. The results in 24-DWP screening showed reduced growth profiles, but comparable viability profiles. Slow growers in 24-DWP achieved the highest productivity improvement upon scaling-up to shake flasks. Product quality of the protein purified from shake flasks and 24-DWP were also compared. The 24-DWP culture conditions were found to influence the levels of acidic species, reduce the G0 N-glycan species, and increase the high-mannose N-glycan species. Nevertheless, the identification of undesirable profiles is executed consistently with the scaled-up culture. We further employed multivariate data analysis to capture differences depending on the two scales and we could demonstrate that cell line profiles were adequately clustered, regardless of the vessel used for the development. In conclusion, the 24-DWP platform was reasonably predictive of the parameters crucial for upstream process development activities, and has been adapted as part of the AbbVie cell line development platform. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:175-186, 2018.

Prediction of filamentous process performance attributes by CSL quality assessment using mid-infrared spectroscopy and chemometrics

Every biopharmaceutical production process aims for control strategies to achieve process robustness in order to ensure consistent product quality. Process variability can origin from process parameters, the biological nature as well as from high lot-to-lot variability of raw materials. In filamentous processes raw materials with very complex matrices, such as corn steep liquor (CSL), are used, which are especially challenging to characterize.In this study, CSL was characterized in detail for its ingredients presenting an overall composition of its matrix of 50 analyzed components (19 amino acids, 5 organic acids, 8 reducing sugars, 7 water-soluble vitamins and 11 trace elements/minerals) in order to facilitate analytical reduction to fingerprinting methods FT-MIR was evaluated as fast and non-destructive spectroscopic fingerprinting method for adequate assessment of CSL quality. Feasibility of this method was shown by the correlation of certain bands in the spectra to substance groups present in CSL, such as the Amide I and II band and amino acids, respectively. Additionally, applicability of FT-MIR could be shown for classification of different CSL lots differing in provider and corn quality as well as for predictability of process performance attributes. The latter was demonstrated on a fed-batch filamentous fungi process for the production of antibiotics. By multivariate data analysis, it could be shown that CSL quality assessment via FT-MIR can be used for the prediction of maximal biomass generated in the process, with a correlation coefficient R2 of 0.964, as well as for the prediction of an unwanted impurity. The combination of a fast and easy method for CSL quality assessment and correlations of this quality with process performance attributes may facilitate the establishment of a risk-based acceptance criteria for raw material quality release of CSL. As CSL is a frequent used raw material, we believe that this method will also be useful for other processes and that CSL quality assessment is of high relevance in academia and industry.

In-situ biogas upgrading process: Modeling and simulations aspects

Biogas upgrading processes by in-situ hydrogen (H2) injection are still challenging and could benefit from a mathematical model to predict system performance. Therefore, a previous model on anaerobic digestion was updated and expanded to include the effect of H2 injection into the liquid phase of a fermenter with the aim of modeling and simulating these processes. This was done by including hydrogenotrophic methanogen kinetics for H2 consumption and inhibition effect on the acetogenic steps. Special attention was paid to gas to liquid transfer of H2. The final model was successfully validated considering a set of Case Studies. Biogas composition and H2 utilization were correctly predicted, with overall deviation below 10% compared to experimental measurements. Parameter sensitivity analysis revealed that the model is highly sensitive to the H2 injection rate and mass transfer coefficient. The model developed is an effective tool for predicting process performance in scenarios with biogas upgrading.

Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language

Modern IT systems have increasingly distributed and dynamic architectures providing flexibility to adapt to changes in the environment and thus enabling higher resource efficiency. However, these benefits come at the cost of higher system complexity and dynamics. Thus, engineering systems that manage their end-to-end application performance and resource efficiency in an autonomic manner is a challenge. In this article, we present a holistic model-based approach for self-aware performance and resource management leveraging the Descartes Modeling Language (DML), an architecture-level modeling language for online performance and resource management. We propose a novel online performance prediction process that dynamically tailors the model solving depending on the requirements regarding accuracy and overhead. Using these prediction capabilities, we implement a generic model-based control loop for proactive system adaptation. We evaluate our model-based approach in the context of two representative case studies showing that with the proposed methods, significant resource efficiency gains can be achieved while maintaining performance requirements. These results represent the first end-to-end validation of our approach, demonstrating its potential for self-aware performance and resource management in the context of modern IT systems and infrastructures.

Thermodynamic equilibrium analysis of entrained flow gasification of spent pulping liquors

The main goal of this work is to investigate if thermodynamic equilibrium calculations can be useful for understanding and predicting process performance and product composition for entrained flow gasification of spent pulping liquors, such as black liquor. Model sensitivity to input data is studied and model results are compared to published pilot plant data. The high temperature and the catalytic activity of feedstock alkali make thermodynamic equilibrium a better predictor of product composition than for many other types of biomass and gasification technologies. Thermodynamic equilibrium calculations can predict the flows of the main syngas and slag products with high accuracy as shown by comparison with experimental data with small measurement errors. The main process deviations from equilibrium are methane formation and sulfur distribution between gas and slag. In order to study real process deviations from equilibrium, it is very important to use consistent experimental data. Relatively small errors in the model input, primarily related to fuel composition, can lead to grossly erroneous conclusions. The model sensitivity to fuel composition also shows that the gasification process is sensitive to naturally occurring feedstock variations. Simulations of a commercial-scale gasification process show that cold gas efficiency on sulfur-free basis can reach over 80 % and that greatly improved efficiency can be obtained by reducing ballast present in the form of water or inorganics.

Do adsorbent screening metrics predict process performance? A process optimisation based study for post-combustion capture of CO2

Recent interest in carbon dioxide capture has led to the development of hundreds of adsorbents. Adsorbent screening is typically performed either by inspecting the isotherms or by using simple adsorbent screening metrics (selectivities, working capacities, figures of merit, etc.). This work seeks to critically evaluate the efficacy of various adsorption metrics for adsorbent screening through the use of process-optimisation. A case study addressing post-combustion CO2 capture using vacuum swing adsorption (VSA) is presented. Four different adsorbents (Mg-MOF-74, UTSA-16, Zeolite 13X and a type of activated carbon) were subjected to process-optimisation studies on a 4-step PSA cycle with light product pressurisation (LPP). Two kinds of process optimisation studies were performed. The first to maximise purity and recovery; and the second to minimise energy consumption and maximise productivity subject to purity/recovery constraints. This study highlights that most commonly used adsorbent metrics do not correctly rank adsorbents in order of their performance at a process scale. Further, it is also shown that N2 affinity/capacity plays a critical role in deciding the process performance of VSA based post-combustion CO2 capture.

Optimal operation of MEA-based post-combustion carbon capture for natural gas combined cycle power plants under different market conditions

Carbon capture for fossil fuel power generation attracts an increasing attention in order to address the significant challenge of global climate change. This study aims to explore the optimal operation under different market conditions for an assumed existing natural gas combined cycle (NGCC) power plant integrated with MEA-based post-combustion carbon capture (PCC) process. The steady state process models for NGCC power plant, PCC process and CO2 compression train were developed in Aspen Plus® to give accurate prediction of process performance. Levelised cost of electricity (LCOE) is formulated as the objective function in optimization studies. Economic evaluation was carried out for the base case of the integrated system including CO2 transport and storage (T&S). The optimal operations were investigated for the carbon capture level under different carbon price, fuel price and CO2 T&S price. The study shows that carbon price needs to be over €100/ton CO2 to justify the total cost of carbon capture from the NGCC power plant and needs to be €120/ton CO2 to drive carbon capture level at 90%. Higher fuel price and CO2 T&S price would cause a higher operating cost of running carbon capture process thus a higher carbon price is needed if targeted carbon capture level is to be maintained.

Iron ore textural information is the key for prediction of downstream process performance

Textural information or information about the presence of porosity, different material or mineral types and their structural arrangement in iron ore is crucial for understanding, predicting and optimising downstream processing performance. Ores with the same chemical and mineral composition may behave very differently during downstream processing due to differences in textural components.To produce a textural description of iron ore, it is preferable to use an automated system to avoid subjectivity and to collect additional information about mineral abundance, liberation and association. CSIRO created a unique dedicated optical image analysis software package for automated textural classification and characterisation of different minerals, sinters and coke. This software, called Mineral4/Recognition4, has been used extensively to collect data for this article.Four case studies of CSIRO research are presented to demonstrate the importance of textural information.•The first example shows that iron ore samples with different texture but similar mineralogy undergo different degrees of assimilation in compact sintering.•The second example shows that empirical modelling of sinter properties was improved considerably after introducing textural information.•The third example demonstrates the application of classification by ore texture to model and optimise hydrocyclone performance.•The last example is an experimental study of ultrasonic treatment of hematitic–goethitic iron ore fines. It demonstrates how the resulting breakdown or deagglomeration of different particles, and the mineral deportment, can be better understood when textural information is also considered.In all cases, the availability of textural information was critical, providing a better prediction of process performance or a deeper understanding of the unit process.

독일 DLR의 듀얼 벨 노즐 특성 및 핵심 변수

Various types of altitude compensation nozzles have been investigated to develop an effective propulsion system. In order to obtain baseline data for future study of dual bell nozzles, main characteristics and key parameters of dual bell nozzles are summarized and described by analysing DLR dual bell nozzles. DLR's experimental researches show that inflection angle is proportional to transition NPR, and extension length is proportional to side load, but inversely proportional to transition NPR and transition duration. Therefore, the nozzle geometry can be determined through the performance prediction process and thus the optimization process is required to meet performance requirements between parameters.

Instrumentation and control of anaerobic digestion processes: a review and some research challenges

To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes require advanced instrumentation and control tools. Over the years, research on these topics has evolved and followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid—mainly industrial—then municipal organic fraction of solid waste and agricultural residues. Time constants of the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. Since fast closed loop control is needed for short time constant processes, human operator is now included in the loop when taking decisions to optimize anaerobic digestion plants dealing with complex solid waste over a long retention time. Control objectives have also moved from the regulation of key variables—measured on-line—to the prediction of overall process performance—based on global off-line measurements—to optimize the feeding of the processes. Additionally, the need for more accurate prediction of methane production and organic matter biodegradation has impacted the complexity of instrumentation and should include a more detailed characterization of the waste (e.g., biochemical fractions like proteins, lipids and carbohydrates) and their bioaccessibility and biodegradability characteristics. However, even if in the literature several methodologies have been developed to determine biodegradability based on organic matter characterization, only a few papers deal with bioaccessibility assessment. In this review, we emphasize the high potential of some promising techniques, such as spectral analysis, and we discuss issues that could appear in the near future concerning control of AD processes.

Modeling of Turning Parameters for Inconel 718 Alloy using ANN

For machining a component, it is important to understand the characteristics of work material in order to choose the appropriate cutting tool and to fix a set of machining parameters to achieve optimum output. Analytical models of machining processes require complete understanding of process mechanism and hence are difficult to be developed. Once developed, these models are useful in parametric optimization, process simulation, operation and process planning, process parameter selection, parametric analysis, process performance prediction, verification of the experimental results, and improving the process performance by implementing/incorporating the theoretical findings. Neural network models associated with artificial intelligence are known as artificial neural networks (ANNs) which are simple mathematical models in the form of defining a function. This work presents the details of the experiments carried out for data acquisition, method of building the ANN models and their validation. These models can be used for predicting the output for a chosen set of input variables or for a specific desired output, finding the set of input variables to be chosen. This work resulted in developing models for the turning process for Inconel 718 alloy in a scientific manner. It also enables further scope of identifying the optimized set of turning parameters for Inconel 718 material using the newly developed coated carbide tools, achieving quality surface and productivity.

A real-time quality monitoring framework for steel friction stir welding using computational intelligence

In this article, we present a human-centric model-based framework, where we create a new process monitoring algorithm relying on discrete frequency-based analysis of process parameters. The algorithm is capable of providing real-time feedback to the process operator in linguistic form (natural language – rule base). The proposed framework is applied to the friction stir welding process, to monitor in real-time for the first time the joining of shipbuilding steel plates (DH36). We take advantage of principles of human-like information capture in granular computing (GrC) and computational intelligence (CI) to (a) build a data-driven model to predict in real-time (during welding) quantitative part quality markers extracted from frequency spectra of the process variables (downward and traverse forces), and (b) we introduce a process monitoring algorithm that takes advantage of the developed model to provide continuous feedback to the operator – in linguistic format – on the performance of the process. We conclude the study by evaluating the proposed approach based on interval type-2 radial basis function neural network (IT2-RBF-NN) against a multilayer perceptron neural network (MLP-NN), and a type-1 radial basis function neural network (T1-RBF-NN). Simulation results show the effectiveness of the proposed approach to handle uncertainties and produce reasonable process performance predictions (∼80% accuracy in testing data) that could be used to further optimise the process.

Measurable Variables in Copper Electrowinning and Their Relevance to Predicting Process Performance

This paper presents a comparative review of process variables and anomalies that directly affect the key performance indicators (KPIs) in copper electrowinning processes. The KPIs under consideration are product quality, production level, and specific energy consumption. This paper describes industrial techniques used for measuring and monitoring those variables and discusses their importance for global process evaluation. The review shows that it is important to improve the measurement of electrolyte variables for a quick and precise diagnosis and better process behavior and production.

A Study of the Evolution of Uncertainty in Product Development as a Basis for Overlapping

Overlapping new product design process is widely applied in industry. However, selections of appropriate overlapping strategies based on the prediction of process performance can be problematic due to insufficient understanding on the dependence between design processes and its effect on the performance. This paper introduces a new model for a product design organization that is based on the evolving nature of the design process, the dependence between up and downstream design specifications, and the design technology being adopted. The model presents an evaluation method for quantifying the downstream evolutionary behavior. Through an industrial case study, it is applied to evaluate how the design performances vary under different overlapping strategies and how to determine an optimal overlapping; the results imply that the performance is contingent on the strategy, and no single strategy outperforms in overall performance measures. Furthermore, the model measures process dependency - a quantification of the downstream work that is indifferent to the change of the upstream. This quantification can be applied to determine the rework probability or rework function proposed by other studies. The model also addresses the rationale of how improving design technology efficiency can lead to an upgrading of design performances.

Effects of ionic strength and ion pairing on (plant-wide) modelling of anaerobic digestion

Plant-wide models of wastewater treatment (such as the Benchmark Simulation Model No. 2 or BSM2) are gaining popularity for use in holistic virtual studies of treatment plant control and operations. The objective of this study is to show the influence of ionic strength (as activity corrections) and ion pairing on modelling of anaerobic digestion processes in such plant-wide models of wastewater treatment. Using the BSM2 as a case study with a number of model variants and cationic load scenarios, this paper presents the effects of an improved physico-chemical description on model predictions and overall plant performance indicators, namely effluent quality index (EQI) and operational cost index (OCI). The acid-base equilibria implemented in the Anaerobic Digestion Model No. 1 (ADM1) are modified to account for non-ideal aqueous-phase chemistry. The model corrects for ionic strength via the Davies approach to consider chemical activities instead of molar concentrations. A speciation sub-routine based on a multi-dimensional Newton–Raphson (NR) iteration method is developed to address algebraic interdependencies. The model also includes ion pairs that play an important role in wastewater treatment. The paper describes: 1) how the anaerobic digester performance is affected by physico-chemical corrections; 2) the effect on pH and the anaerobic digestion products (CO2, CH4 and H2); and, 3) how these variations are propagated from the sludge treatment to the water line. Results at high ionic strength demonstrate that corrections to account for non-ideal conditions lead to significant differences in predicted process performance (up to 18% for effluent quality and 7% for operational cost) but that for pH prediction, activity corrections are more important than ion pairing effects. Both are likely to be required when precipitation is to be modelled.