Process Performance Model Research Articles

A novel integrated process-performance model for laser welding of multi-layer battery foils and tabs

A novel integrated process-performance model for laser welding of multi-layer battery foils and tabs

Impact of salinity on anaerobic ceramic membrane bioreactor for textile wastewater treatment: Process performance, membrane fouling and machine learning models

Anaerobic membrane bioreactor (AnMBR) shows great potential for textile wastewater treatment, but high salinity in the influent may undermine its performance. This study evaluated the impact of salinity on the treatment performance of an upflow anaerobic sludge blanket (UASB) configured AnMBR using a flat sheet ceramic membrane. The salinity was stepwise increased (0, 5, 10 and 20 g/L) in four phases of the AnMBR operation. Results indicated that increased salinity jeopardized the COD removal efficiency of AnMBR from 92% to 73%, but had a marginal effect on dye removal efficacy (90–96%). Low salinity (5 g/L) boosted the biogas production whilst high salinity (>10 g/L) had a negative impact. Additionally, the increase of salinity resulted in the soluble microbial production (SMP) concentration soar and membrane fouling rate increase, peaking at a salinity of 10 g/L (Phase III) and recovering back to a lower level at a salinity of 20 g/L (Phase IV). This indicated a transition occurrence at a salinity of 10 g/L (Phase III). The microbial diversity analyses further suggested a transition from salinity-sensitive microbes (Aminiphilus, Caldatribacterium, Mesotoga, Methanobrevibacter, Methanobacterium, Methanosaeta) to salinity-tolerant microbes (Longilinea, Ignavibacterium, Rhodovarius, Bosea and Flexilinea). This transition can be associated with the increase SMP concentration and more severe membrane fouling in Phase III, which were mitigated after a new equilibrium was reached when the microbial consortium acclimatized to the high salinity. Finally, a machine learning model of the Adaboost algorithm was established to predict COD removal under different salinities. Importantly, this study revealed that AnMBR process performance and membrane operation can be maintained for high salinity textile wastewater treatment with a halophilic microbial community growth under high-salinity selection pressure.

Process Simulation and Performance Models for Enhanced Modular Refinery Operations in Nigeria

The study aimed at availability of petroleum products in Nigeria through the operation of enhanced modular refinery process due to inefficient conventional major refineries. The enhanced modular refinery converts or processes residue product from conventional modular refinery as feedstock to the hydrocracker reactor for viable and desired products such as liquefied petroleum gas, naphtha and diesel. Therefore, twenty Nigerian crude oil types were classified as sweet, light and medium crude oil, and these crude oil types were categorized based on their recovery volume at true boiling point of 370oC as Group A, Group B and Group C respectively. Thus, based on product output and equipment cost, a modular refinery with 29 trays was used in this study, as light and medium sweet crude oil types were simulated in a modular refinery of various column trays prior to the desired tray. A topping plant with a 30,000 barrel per day capacity and a modified topping plant with 29 trays respectively were used to process different types of Nigerian crude oil. using Aspen Hysys to evaluate their products yield and tray compositions. The modified modular refinery with hydrocracker yielded more valuable from the residue of conventional modular refinery with minimal bottom fraction. Performance models for hydrocracker reactor was developed based on the nature of reaction, kinetic parameters estimated and results compared with experimental data with minimum deviations. The developed performance models predicted feedstock conversion and product yield along the hydrocracker reactor’s dimensionless length by solving a set of ordinary differential equation models. Thus, the hydrocracking process was simulated to evaluate the effects of catalyst effectiveness factor on feedstock conversion and products yield.

An integrated process-performance model of ultrasonic composite welding based on finite element and artificial neural network

Abstract Ultrasonic welding is a well-established technique for joining polymers and has recently been utilized to join carbon fiber composites in the aerospace and automotive industries. Process and performance models are important to understand the joining mechanisms and joint performance of ultrasonic composite welding. This paper presents an integrated process-performance model, which is composed of a finite element-based process model to calculate the weld area and an artificial neural network-based performance model to predict the weld quality. This integrated model provides a solution to simultaneously obtain the weld formation and the weld performance information in a relatively fast way.

Ionic Liquid Solvent Design Framework for Extraction of Phytochemicals using Microwave-Assisted Method

Computer aided molecular design (CAMD) has been used widely for solvent design. It is a reverse approach in the selection of solvents for real application. CAMD is suitable for ionic liquid solvent design due the vast possibilities of ionic liquid molecular structures to be identified. Ionic liquid has broad ranges of applications especially in separation especially in carbon capture and azeotropic separation. This is due to the unique structures of ionic liquid that can be tailored for specific product separation. This study focuses on ionic liquid design framework for phytochemical extraction, incorporating the prediction of the extraction yield using the microwave-assisted method. The framework was developed in several stages. Stage 1 identifies the user needs, problems and constraints as well as target properties. Stage 2 comprise of comprehensive database development for ionic liquid and phytochemical properties; while property models' library was developed in Stage 3. Stage 4 involved the development of solvent design algorithm for ionic liquid selection for the targeted process. In Stage 5, depending on the type of extraction method considered, either using normal Soxhlet or a microwave-assisted extraction, the extraction yield can be predicted using the process performance model. The performance model for the liquid extraction is the thermodynamic solid-liquid equilibria model while for the advanced extraction method, the model was obtained through optimization of the experimental extraction process. This systematic framework is illustrated through a case study involving flavonoid and phenolic acid extraction from Ortosiphon aureus. Based on the yield prediction, 1-ethyl-3- propylimidazoilum bromide can extract 29.92 mg/g and was selected as a solvent for Flavonoid extraction using Microwave-assisted extraction. The design framework is able to find the optimal ionic liquid candidate for the extraction process.

Lean Six Sigma applied to process performance and improvement model for the development of electric scooter water-cooling green motor assembly

In response to the environmental issues triggered by global warming, worldwide companies gradually put the factor of carbon emission into the process of product lifecycle, developing green technology or adopting cleaner production aimed at sustainable development. Lean Six Sigma has advantages of cutting waste and facilitating process improvements as well as system analysis, helping enterprises create the overall business benefits in the value chain. Used in the renewable energy industry, it can promote the triple bottom line (TBL), the performance of sustainable production for corporate profit, social responsibility and environmental responsibility. Therefore, this work took the process performance of the electric scooter water-cooling green motor manufactured in Taiwan with the world’s highest density of scooters as a case study. The developed performance evaluation and improvement model for manufacturing scheduling and process quality achieved the goal of economic benefits of enhancing process quality performance by shortening manufacturing scheduling and reducing process variations with Lean Six Sigma. Besides, they could respond to the policy of energy saving and carbon reduction – replacing the traditional scooters of high-carbon emissions with the electric scooters of low emissions. Furthermore, they could bring enterprises into harmony with economic benefits, ecological benefits and social benefits.

Process performance and multi-kinetic modeling of a membrane bioreactor treating actual oil refinery wastewater

The performance of a membrane bioreactor treating oily wastewater generated by a petroleum refinery was investigated at mixed liquor suspended solids concentrations of 6.5 and 8.5 g/l with hydraulic retention times ranging from 12 to 24 h. An evolutionary polynomial regression technique and multi-kinetic models consisting of the first-order, Grau second-order, and modified Stover-Kincannon were applied for predicting the performance of the bioreactor. The results showed that a maximum chemical oxygen demand reduction of 97% is achievable at the mixed liquor suspended solids of 8.5 g/l and hydraulic retention time of 24 h. The results also revealed positive influence of higher mixed liquor suspended solids and hydraulic retention time values on the bioreactor performance. In addition, the high accuracy of the evolutionary polynomial regression evidenced its capability in investigating the membrane bioreactor. Based on the analysis performed by the resulted model from the evolutionary polynomial regression, the optimum operating conditions (hydraulic retention time of 21 h; mixed liquor suspended solids of 8.2 g/l) were recommended. According to the kinetic evaluation, the Grau second-order and modified Stover-Kincannon kinetics seemed to be the best approach in describing the chemical oxygen demand reduction in the membrane bioreactor.

A hybrid constructed wetland for organic-material and nutrient removal from sewage: Process performance and multi-kinetic models

A pilot-scale hybrid constructed wetland with vertical flow and horizontal flow in series was constructed and used to investigate organic material and nutrient removal rate constants for wastewater treatment and establish a practical predictive model for use. For this purpose, the performance of multiple parameters was statistically evaluated during the process and predictive models were suggested. The measurement of the kinetic rate constant was based on the use of the first-order derivation and Monod kinetic derivation (Monod) paired with a plug flow reactor (PFR) and a continuously stirred tank reactor (CSTR). Both the Lindeman, Merenda, and Gold (LMG) analysis and Bayesian model averaging (BMA) method were employed for identifying the relative importance of variables and their optimal multiple regression (MR). The results showed that the first-order–PFR (M2) model did not fit the data (P > 0.05, and R2 < 0.5), whereas the first-order–CSTR (M1) model for the chemical oxygen demand (CODCr) and Monod–CSTR (M3) model for the CODCr and ammonium nitrogen (NH4−N) showed a high correlation with the experimental data (R2 > 0.5). The pollutant removal rates in the case of M1 were 0.19 m/d (CODCr) and those for M3 were 25.2 g/m2∙d for CODCr and 2.63 g/m2∙d for NH4-N. By applying a multi-variable linear regression method, the optimal empirical models were established for predicting the final effluent concentration of five days' biochemical oxygen demand (BOD5) and NH4-N. In general, the hydraulic loading rate was considered an important variable having a high value of relative importance, which appeared in all the optimal predictive models.

Process Performance Models in Software Engineering: A Mathematical Solution Approach to Problem Using Industry Data

An IT Project Manager is responsible for project planning, estimating and scheduling, developing and monitoring the progress throughout its development life cycle. The selection of a particular methodology is heuristic and the performance of the system developed is unpredictable. The authors suggest that a designed process performance model (PPM) can help to predict the required factors of a process to help achieve set goals for the process. This, in turn, can help to control factors that the project and the organizations need to control and ensure expected results. PPMs may enable to work out the relationship between different variables for a well-defined project and this knowledge becomes basis for prediction of performance solution, and helps in implementation of solution. This approach related to designing of PPMs, for various real life projects situations has not been attempted by industry in a big way. The authors demonstrate how to work out the PPMs, based on the given inputs of projects, by an Indian IT company. The solution works out number of bids which arrive at a given time or predict when the next bid will arrive at service centre, based on time series and queuing theory approach. This solution approach is based on different problems that will become the basis to build PPMs for similar problems. The problems discussed here are from an Information Technology Company, with real life data from the projects under development. Testing these models with more projects data thus will formalize how to build PPMs in a similar way. The authors discuss problem areas where time series and queuing theory Models can be applied and benefits of the present approach. The authors have similarly worked on different mathematical models based on industry data and build PPMs based on Bayesian, regression, fuzzy logic and other models. This paper is submitted with only two models just to prove the concept. In future building PPMs is likely to be a necessity in high maturity IT organizations.

The examination of key performance indicators of warehouse operation systems based on detailed case studies

Logistics corporations have very important roles in the current economic development for most countries around the world, but they also have encountered intense competition. Usually, such industries use key performance indicators (KPIs) to pursue their business performance. Several theoretical methods have been used by researchers to study the KPIs for industries, but it is very difficult for the industries to utilize these theoretical methods, and the related research results may not be suitable for the industries. This research first developed a ‘process performance model’ for a warehouse management system, and then extensive case studies were conducted with respect to four different types of logistics corporations.In order to develop the KPIs for logistics corporations, we used case studies to identify the critical functions and processes for a warehouse management system. Based on the developed ‘process performance model’ and the identified critical functions and processes, we proposed eight KPIs that are focused on the quality, accuracy, costs, security, and timeliness of the operations of warehouse management systems. Besides the KPIs, logistics corporations also need to emphasize the implementation of daily management performance indicators (DMPIs) and then integrate the applications of KPIs and DMPIs, which will result in synthesized business performance.

Parametric Optimization in Wire Electrical Discharge Machining of Titanium Alloy Using Response Surface Methodology

Wire Electrical Discharge Machining (WEDM) is extensively used for machining of complex shapes in the field of die and mould making, medical, aerospace and automobile industries for machining of too hard materials. Improper selection of WEDM process parameters setting can affect the machining efficiency and surface roughness due to arcing phenomenon that lead by electrical discharge. Present study has been made to optimize the process parameters during machining of titanium (Ti-6Al-4V) alloy by WEDM using response surface methodology (RSM). Five input process parameters of WEDM namely Pulse on time, pulse off time, servo voltage, input power and wire tensionwere chosen as variables to study the process performance in terms of kerf width, material removal rate (MRR), surface roughness. The analysis of variance (ANOVA) was carried out to study the effect of process parameters on process performance and mathematical models havebeen developed for response parameters. Properties of the machined surface have been examined by the scanning electron microscope (SEM).The experimental results revealthat the kerf width increases as the pulse on time, input power, server voltage and wire tension increases and the MRR increases as the pulse on time and input powerincreases. It is observed that as the pulse on timeand input power increases, the surface roughness also increases. Further it is observed that as and when the wire tension and servo voltage increases the surface roughness decreases and it improves the quality of machined surface.

A METHODOLOGY FOR QUANTITATIVELY MANAGING THE CODING PHASE OF SOFTWARE DEVELOPMENT PROCESS

Many organizations use information technology to gain competitive advantage. As a result the demand for software products increased tremendously and the information technology industry has grown rapidly. As demand increased, the competition among information technology firms also increased. The information technology companies can no longer survive by just delivering the products but has to ensure the quality of products as well as the products have to be delivered on time without cost or effort overrun. Hence it is imperative for information technology companies to quantitatively manage the software development process. In fact quantitative project management is one of the requirements for achieving higher levels of capability maturity model. Lots of research has been carried out in the past to develop models to quantitatively manage the software development process. Most of these studies focussed on methodologies to quantitatively manage only one of the performance characteristics namely quality or schedule or effort. But to deliver the good quality software on time within the budgeted cost, all the critical performance parameters of software development process namely quality, productivity, effort, cost, etc need to be managed simultaneously. Many of these characteristics are related to each other and many cases the correlation is such that improving the performance of one characteristic will adversely affect the performance of other performance characteristics. Moreover all these performance characteristics need to be managed by controlling a common set of control parameters. Hence it is required to identify the best values of the control parameters which would simultaneously optimise all the performance characteristics. In this paper, the authors suggest a methodology to simultaneously optimize the performance characteristics of coding phase of the software development process. The same methodology can be used to simultaneously manage the different performance characteristics at other phases as well as the overall software development process. In this study the authors have taken two performance characteristics namely coding productivity and quality (measured in terms of defect density). The approach is to develop separate process performance models to estimate the coding productivity and defect density using the process parameters namely programmer skill, reviewer skill, review type, preparation time, module complexity and code review rate. Then the values of these process parameters which would simultaneously optimize the coding productivity and defect density are identified using Taguchi’s loss function. The proposed approach has been implemented on seven software development projects and the results are very encouraging. Moreover the optimum obtained by the proposed method is much better than that of optimizing the coding productivity and defect density separately. The project managers also agreed that a common setting for process parameters which would optimize both performance characteristics simultaneously is much easier to implement than methods for managing different performance characteristics independently.

Process performance model for predicting Delivered Defect Density in a software scrum project

Agile scrum projects are gaining popularity for software development and delivery, due to its inherent attributes of flexibility in execution and quicker time to market. However, the critics of Agile have been raising questions on the predictability of success of Agile execution as the emphasis given to quantitative management using predictive techniques for an Agile project may not be to the extent of large mission critical projects which uses in-process leading indicators to predict outcomes. One possible solution to this challenge is to use the best practices from industry adopted models like the Capability Maturity Model Integration® for planning and managing Agile software delivery. In this paper, we present a study were a prediction model was developed for use in Agile projects for systematic in-process monitoring and decision making. The purpose of this paper is to propose a predictive model as a driver for continuous improvement of Delivered Defect Density in the context of a Scrum project.Our findings show that using the prediction model, the success of Agile project could be forecasted and controlled during its planning and execution phases as against waiting for the final release to The paper starts with defining the background and context of the study, research methodology and study of existing literature in this area. A brief description of the major knowledge areas considered in the study namely Agile, CMMI and High Maturity practices in CMMI is then presented. An introduction on measures, metrics and prediction models is also provided. The development of the Agile Prediction Model and the application of the model Agile SCRUM projects is addressed as the main theme of the paper which concludes with the advantages, shortcomings and limitations of the model developed. 1. Background of the study In organizations where quality of software is a primary goal for the software projects, it is useful to measure and observe Delivered Defect Density as a lagging indicator signifying the quality of developed software. Though this is a good metric to indicate the goodness of the delivered software, the metric lacks the ability to be controlled, as it is an outcome of the software development process and not an in-process metric. In this context, the need for in-process or leading indicators which will help practitioners get visibility on the outcome is important. In this paper, the processes and sub-processes leading to Delivered Defect Density is

Analysis and Optimization based on Reusable Knowledge Base of Process Performance Models.

In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires developing automated methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by prototyping a decision-support system for process engineers. The decision support system allows users to hierarchically compose and optimize dynamic production processes via a graphical user interface.

Modeling and Optimization of Manufacturing Process Performance using Modelica Graphical Representation and Process Analytics Formalism.

This paper concerns the development of a design methodology and its demonstration through a prototype system for performance modeling and optimization of manufacturing processes. The design methodology uses a Modelica simulation tool serving as the graphical user interface for manufacturing domain users such as process engineers to formulate their problems. The Process Analytics Formalism, developed at the National Institute of Standards and Technology, serves as a bridge between the Modelica classes and a commercial optimization solver. The prototype system includes (1) manufacturing model components' libraries created by using Modelica and the Process Analytics Formalism, and (2) a translator of the Modelica classes to Process Analytics Formalism, which are then compiled to mathematical programming models and solved using an optimization solver. This paper provides an experiment toward the goal of enabling manufacturing users to intuitively formulate process performance models, solve problems using optimization-based methods, and automatically get actionable recommendations.

Quantitative Analysis of the Process Models in IT Service Provision Engineering

Nowadays, the provision of IT service is becoming the main business of IT enterprise operation, especially in E-commerce; however, the study on its quantitative modelling method is still in its infancy. This paper studies key subprocesses affecting IT service provision from the aspect of incident process, and provides a quantitative solution for enterprises to modular IT service by designing key subprocesses, establishing measurement metrics and process performance models across full lifecycle. In the end, the numerical analysis and measured data proves that the model has a relatively high goodness-of-fit.

A design approach for process-based knowledge management systems

PurposeThis paper presents a design approach for process-based knowledge management (PKM) systems that can support knowledge-intensive processes where effective task execution is highly reliant on the knowledge and expertise of participants executing the tasks. The proposed design approach includes design methods and kernel theories governing the design of PKM systems and can also be easily integrated with existing systems analysis and design techniques.Design/methodology/approachThe design science research methodology is used to design and develop the artifact which includes the overall PKM design approach. Information systems design theory is used as a high-level framework to develop and structure the design approach. Relevant design methods and behavioral theories are reviewed to identify kernel theories that guide the design and development of PKM systems. The design approach consists of meta-requirements for PKM systems and design processes to achieve the meta-requirements. A feasibility study is conducted to evaluate the feasibility of the proposed approach.FindingsThe design approach presented in this paper can guide system analysts and system developers in the design of knowledge management systems for supporting knowledge-intensive processes. The paper also includes a comprehensive design theory for PKM systems consisting of meta-requirements and a synthesis of various kernel theories into actionable design procedures. The proposed procedures include knowledge requirements modeling, knowledge flows modeling and knowledge and process performance modeling procedures. The feasibility study indicates that the PKM approach can be more useful and effective than solely using unified modeling language (UML)-based systems analysis and design techniques for the design of PKM systems.Research limitations/implicationsAn implication to information systems design research is the feasibility of developing a specialized design approach that incorporates significant domain knowledge to solve complex information system design problems. An implication to practice is the significant potential to improve productivity and effectiveness of systems analysts and designers in developing PKM systems. A limitation is the small sample size of the feasibility study used to evaluate the ease of use and utility of the design approach.Originality/valueThe study makes a unique contribution by proposing a design approach that integrates business process and knowledge management considerations. The approach is particularly valuable because of the focus on integration with existing systems analysis and design techniques, thus allowing for easier adoption.

Capacity and availability management by quantitative project management in the IT service industry

PurposeThe purpose of this paper is to develop and implement an efficient capacity and availability management tracker for information technology (IT) service delivery management that can be applied by analyzing base‐lined data, using quantitative project management and knowledge discovery techniques, for taking decisions on a monthly basis in resource allocation, optimum resource utilization and efficient service level management.Design/methodology/approachA ticket forecasting model has been developed. Also data were collected from fixed price running IT service delivery programs with about 200 or more full‐time employees working in each program, limited to four large service lines. Using Monte Carlo simulation, the data were base lined and applied to a capacity and availability management tracker. The results were then analyzed and conclusions drawn.FindingsThe findings suggest that the service provider was able to share the resources across the organization as needed based on demand, and overall the bench strength of the organization was drastically reduced. Also they were able to achieve better service level management. This has contributed to profit margin improvement in the organization.Research limitations/implicationsThe relatively small sample size (three programs and four service lines in one IT service organization) has impact on research implications.Practical implicationsThe results have implications for practice in promoting IT tools and technique for capacity and availability management in an IT service provider. It is suggested that senior management may need to concentrate on adapting a positive approach promoting the usage of new IT tools and techniques in order to achieve IT service improvement, better performance and margin improvement.Originality/valueThis study has addressed the consequences of initiating an organization‐wide knowledge‐based process performance model for a popular strategic IT initiative. The study has answered the questions on better resource utilization and efficient service level management.

Analyse usage of process performance models to predict customer satisfaction

The key parameter to differentiate the vendor capability in an IT industry is the value add that the customer experiences. To achieve this, it needs to be understood, what are the key parameters that drive this and what needs to be done to improve it. Process performance models help to understand customer satisfaction, it is a quantitative research, the associated process and probable parameters help in improving customer satisfaction. This process is established using a case study. The importance of process performance models in not clearly understood by the project team and exposure to lack of understanding of associated sub process is clearly articulated. Based on these gaps, the intent to identify the right sub process and models is addressed through a project plan.

Using Pattern Perspective to Model Key Business Process Performance

Web service composition is a more and more promising solution for building distributed applications on the e-business processes. It has been recognized as a flexible way for resource sharing and application integration. As the number of functional similar Web services increases, after Web services be selected that can best meet the requirements of the consumers, how to construct a kind of key business process performance (KBPP) model has been an ongoing research direction in business process performance management for Web services. In this paper, the key business process performance model is proposed, which describes the key performance of business with specific features (non-functional characteristics or QoS) based on pattern perspective. This new approach helps developers to direct related engineering implementation.