Process Performance Indicators Research Articles

CO2 Adsorption on Biomass-Derived Carbons from Albizia procera Leaves: Effects of Synthesis Strategies.

CO2 capture is a useful strategy for controlling the risks associated with global warming. The design of an adsorbent is essential for clean and potentially energy-efficient adsorption-based carbon capture processes. This study reports a facile and moderately temperature single-stage combined pyrolysis and activation strategy for the synthesis of nitrogen-doped carbons for high-performance CO2 capture. Using nitrogen-rich Albizia procera leaves as the precursor and carrying out single-stage pyrolysis and activation at temperatures of 500, 600, and 700 °C in the presence NaHCO3 as an activating agent, carbons with different surface characteristics and ultrahigh weight percentage (22-25%) of nitrogen were obtained. The subtle differences in surface characteristics and nitrogen content had a bearing on the CO2 adsorption performance of the resultant adsorbents. Outstanding results were achieved, with a CO2 adsorption capacity of up to 2.5 mmol/g and a CO2 over N2 selectivities reaching 54. The isotherm results were utilized to determine the performance indicators for a practical vacuum swing adsorption process. This study provides a practical strategy for the efficient synthesis of nitrogen-doped carbons for various adsorption applications.

Методическая основа комплексной оценки результативности бизнес-процессов в управлении качеством предприятия

The article explains the specifics of forming a methodology for the comprehensive assessment of key perfor-mance indicators of business processes in quality management. The relevance of the study is due to the fact that the existing methods of foreign and domestic scientists for calculating and evaluating business process indicators do not affect their relationship with the quality management of enterprises and do not describe the procedure for their automation. In addition, the author argues for the methodology of comprehensive assess-ment of key performance indicators of business processes of the quality management system, consisting of several stages: identification of processes, determination of their performance, expert evaluation of indicators, carrying out calculations according to specified formulas. An example of automating the calculation of key per-formance indicators of the business process “monitoring and measurement” of an industrial enterprise is pre-sented. This methodology allows to consider the strengths and weaknesses of the enterprise’s business pro-cesses for further development of corrective action plans to improve the quality management system.

Process Capability and Performance Indices for Discrete Data

Process capability and performance indices (PCIs and PPIs) are used in industry to provide numerical measures for the capability and performance of several processes. The majority of the literature refers to PCIs and PPIs for continuous data. The aim of this paper is to compute the classical indices for discrete data following Poisson, binomial or negative binomial distribution using various transformation techniques. A simulation study under different situations of a process and comparisons with other existing PCIs for discrete data are also presented. The methodology of computing the indices is easy to use, and as a result, one can have an assessment of the process capability and performance without difficulty. Three examples are further provided to illustrate the application of the transformation techniques.

Performance indicators of medical processes with strategic alignment in patient care

Performance indicators of medical processes with strategic alignment in patient care

Tribological aspects of nano-particle fluid based cooling and lubrication in milling process

The introduction of different additives into the cooling and lubrication systems in the metal cutting process, contributed to improving the machining process sustainability. Different types of additives can help to reduce friction, reduce wear of contact surfaces, improve heat dissipation from the cutting zone, etc. With the development of nanotechnology, great focus has been placed on the investigation and using of nano-particles, nano-additives and nanofluids in industry. In this paper, the effects of using the nano-particle based cooling and lubrication fluid are investigated. There are performed comparison effects of standard cooling and lubrication fluids versus nanofluids based cooling and lubrication on cutting process performance. Molybdenum disulphide particles are used as nano-particles, which dispersed in a standard cooling and lubrication fluid. As workpiece was used alloyed tool steel. Milling process performance indicators was analysed based on Taguchi experimental plan, for different cooling and lubrication condition and process parameters. Cutting forces component in three perpendicular axes, and machined surface parameters were measured. Experimental analysis has shown that the use of nano-particles, which mixed in a standard cooling and lubrication fluid, significantly contributes to the reduction of cutting forces, friction coefficients, more efficient cutting and generation of machined surfaces.

Anaerobic digestion of untreated manure: Environmental risk assessment of resultant digestates

Anaerobic digestion (AD) of poultry and goat manure was performed to estimate the biochemical methane potential (BMP) and biosafety of the digestates using standard analytical and microbiological methods. The effects of residence time (RT) on BMP, process performance indicators, potential pathogens and indicator bacteria reduction as well as heavy metals concentrations were determined. The experiment was performed in a semi-batch mode at mesophilic temperature of 30 ± 0.2°C using 20 L prototype biodigesters over 45 days RT. The cumulative biogas yield from goat manure (31,703 ml/gVS) was > yield from poultry manure (30,275 ml/gVS). The process performance indicators after digestion revealed a minimal variation in pH (6.0 to 7.5) with notable reduction in total solids (55.0% in goat manure >50.6% in poultry manure) and volatile solid (56.1% in goat manure > 44.2% in poultry manure). Besides methanogens (Methanothrix, Methanobacterium and Methanosarcina species), Bacillus (100%) and Clostridium (87.5%) species were the most predominant bacterial genera. Sanitary assessment revealed a significant reduction (p < 0.05) of indicator and potentially pathogenic bacteria at residence time ≥ 30 days. At 45 days RT, faecal coliform, Staphylococcus and Vibrio species were undetected in both poultry and goat manure digestates, while total coliforms (3.6 log CFU/ml) and Salmonella count (3.2 log CFU/ml) in poultry manure digestate were above tolerable limit. A negligible amount (p < 0.05) of heavy metals was observed with higher zinc and copper concentrations in poultry and goat manure respectively. Extension of residence time and/or further treatment is critical to ensure digestate meets the United States EPA/EU permissible limit of 3.0 log CFU/ml before farmland application

Data mining in traditional Chinese medicine product quality review

The traditional Chinese medicine(TCM) enterprises have accumulated a large amount of product quality review(PQR) data. Mining these data can reveal the hidden knowledge in production and helps improve pharmaceutical manufacturing technology. However, there are few studies involving the mining of PQR data and thus enterprises lack the guidance to analyze the data. This study proposed a method to mine the PQR data, which consisted of 4 functional modules: data collection and preprocessing, risk classification of variables, risk evaluation by batches, and the regression analysis of quality. Further, we carried out a case study of the formulation process of a TCM product to illustrate the method. In the case study, the data of 398 batches of products during 2019-2021 were collected, which contained 65 process variables. The risks of variables were classified according to the process performance index. The risk of each batch was analyzed through short-term and long-term evaluation, and the critical variables with the strongest impact on the product quality were identified by partial least square regression. The results showed that 1 variable and 13 batches were of high risk, and the critical process variable was the quality of the intermediates. The proposed method enables enterprises to comprehensively mine the PQR data and helps to enhance the process understanding and improve the quality control.

Virtual Reality Based Decision Support Model for Production Process of Museum Exhibition Projects

Quality and time are uncompromisable performance indicators of the physical exhibition production process. Design changes during the installation phase of the process, which arise from poor design representations, cause expensive rework and therefore cost increase. Since non-AEC (architecture/engineering) construction) decision-makers can only partially imagine the outcome through traditional 2D and 3D design representations; we proposed a model incorporating virtual reality technologies to solve design miscommunications and improve stakeholder participation. We adopted a qualitative method and tested the model through case studies with five expert museum professionals. We confirm that VR enhances the design communication among the contributing professionals and, unlike other traditional methods, makes it possible to present multimedia, lighting, sound effects, and other digital elements throughout the exhibition production process. Findings indicate that VR has the potential for significant cost reductions and quality improvement in the construction phase of the exhibitions.

Estimation of process performance index for the two-parameter exponential distribution with measurement error

Measurement errors are inevitable in practice, but they are not considered in the existing process performance index. Therefore, we propose an estimation method of process performance index for the two-parameter exponential distribution with measurement errors to fill this gap. In this paper, the relationship between the unobservable actual value and measurement value is considered as full error model, and the maximum likelihood estimation method is considered to obtain the unknown parameters. In addition, we also use the Bootstrap method to construct confidence intervals of process performance index. The performance of the proposed estimation is investigated in terms of bias, mean square error (MSE) and average interval length. Simulation results show that the proposed estimator outperforms other estimators. Finally, an example of the mileage data of the military personnel carrier is given to illustrate the implementation of the proposed estimation method.

Study of the Performance of Wire EDM on Titanium alloy using Taguchi Method

In this work, an experiment using wire electro-discharge machining to analyse titanium alloy is described (WEDM). The goal is to examine how various process factors affect a number of process performance indicators, including pulse width, servo reference voltage, pulse current, and wire tension (such as cutting speed, wire rupture and surface integrity).The Taguchi approach was used. Charmilles WEDM has been used in every trial. It was also shown that both peak current and pulse interval can speed up cutting. It has been demonstrated that surface roughness rises with pulse width and falls with pulse interval.

Lean strategy for data mining and continuous improvement of Chinese pharmaceutical process: a case study of sporoderm-removal Ganoderma lucidum spore powder

In the digital transformation of Chinese pharmaceutical industry, how to efficiently govern and analyze industrial data and excavate the valuable information contained therein to guide the production of drug products has always been a research hotspot and application difficulty. Generally, the Chinese pharmaceutical technique is relatively extensive, and the consistency of drug quality needs to be improved. To address this problem, we proposed an optimization method combining advanced calculation tools(e.g., Bayesian network, convolutional neural network, and Pareto multi-objective optimization algorithm) with lean six sigma tools(e.g., Shewhart control chart and process performance index) to dig deeply into historical industrial data and guide the continuous improvement of pharmaceutical processes. Further, we employed this strategy to optimize the manufacturing process of sporoderm-removal Ganoderma lucidum spore powder. After optimization, we preliminarily obtained the possible interval combination of critical parameters to ensure the P_(pk) values of the critical quality properties including moisture, fineness, crude polysaccharide, and total triterpenes of the sporoderm-removal G. lucidum spore powder to be no less than 1.33. The results indicate that the proposed strategy has an industrial application value.

Monitoring intracellular metabolite concentrations by moving horizon estimation based on kinetic modeling

The biotechnology industry can significantly benefit from new paradigms such as smart manufacturing, digitalization and quality-by-design to render more competitive and robust processes. Real-time monitoring of key process parameters and performance indicators can facilitate the transition toward smart biomanufacturing. Since cells are typically used to catalyze biotechnological processes, online monitoring of the cell's health and intracellular metabolic status is of interest to the bioprocess industry. However, intracellular monitoring is challenging due to the intrinsic physical limitation imposed by the cell wall/membrane and the long time required for analytical measurements. This work outlines how soft sensors based on moving horizon estimation allow inferring the intracellular metabolite concentrations in bioprocesses, even for advanced or complex metabolic systems. For example, it can be applied for monitoring metabolic cybergenetic systems, whereby metabolic pathways are dynamically regulated via genetic circuits and external inputs. The moving horizon estimator uses a kinetic model of the central carbon and energy metabolism that describes the dynamics of the intracellular metabolites. We underline the use of moving horizon estimation considering the anaerobic fermentation of Escherichia coli with optogenetic regulation of the adenosine triphosphate turnover. With the information on the extracellular substrate, product and biomass concentrations, we could reconstruct the internal cell's metabolic state with a good performance.

Collaborative Optimization Control of Torque Unloading and Loading for a Dual-Motor Coupling Drive System

Taking a dual-motor planetary two-gear transmission drive system as the research object, this paper analyzes the factors influencing gear shift process and major performance indexes. The study focuses on the influence of torque unloading and loading process on vehicle impact during gear shift stage. In view of the collaborative operation of main motor, auxiliary motor and gear shift actuator of transmission, the author proposed a control method and strategy process featuring multiple collaborative gear-shift torque. Then, a simulation platform was built for the vehicle and power system to simulate and compare torque unloading and torque recovery under specified acceleration condition. Compared with non-collaborative torque control, the vehicle impact under multiple collaborative control was reduced by 60% in torque unloading stage and 80% in torque recovery stage. Further, the prototype was developed for transmission control unit and power system and tested on the vehicle. The test results further demonstrated that multiple collaborative gear-shift torque control could effectively reduce the vehicle impact during unloading and loading, with gear shift time shortened and gear shift comfort improved.

A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs

Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment Plants (WWTPs) have gained importance. The treatment process in WWTPs is complex, consisting of several stages, which consume considerable amounts of resources, mainly electrical energy. Minimizing such energy consumption while satisfying quality and environmental requirements is essential, but it is a challenging task due to the complexity of the processes carried out in WWTPs. One form of evaluating the performance of WWTPs is through the well-known Key Performance Indicators (KPIs). The KPIs are numerical indicators of process performance, being a simple and common way to assess the efficiency and eco-efficiency of a process. By applying KPIs to WWTPs, techniques for monitoring, predicting, controlling, and optimizing the efficiency and eco-efficiency of WWTPs can be created or improved. However, the use of computational methodologies that use KPIs (KPIs-based methodologies) is still limited. This paper provides a literature review of the current state-of-the-art of KPI-based methodologies to monitor, control and optimize energy efficiency and eco-efficiency in WWTPs. In this paper, studies presented on 21 papers are identified, assessed and synthesized, 12 being related to monitoring and predicting problems, and 9 related to control and optimization problems. Future research directions relating to unresolved problems are also identified and discussed.

Process-informed adsorbent design guidelines for direct air capture

Direct air capture using solid adsorbents is a proven technology critical to reducing our net greenhouse gas emissions to zero and beyond. Currently, academic research into the technology mainly focuses on the development of new adsorbents. However, there is a discord between the adsorbent design and process performance. Many materials scientists focus on maximising metrics such as the CO2 capacity of their adsorbent. Here, we combine detailed process modelling, machine learning, and extensive global sensitivity analysis, which entails varying all of the model parameters together, on a direct air capture process to show that the dry CO2 adsorption capacity does not influence process performance for an amine-functionalised adsorbent operating in a temperature vacuum swing adsorption (TVSA) process, while it is important in a steam-assisted TVSA (S-TVSA) process. In fact, adsorption kinetics, density, and thermal conductivity are all critical attributes to obtaining a low energy penalty and reduced costs. The analysis also highlights the importance of heat transfer, directing process engineers to (alternative) adsorber designs that maximise this. By an in-depth evaluation of how process performance indicators are affected by materials properties and process operating parameters, this work provides guidance to both material scientists and process engineers towards the design of a “unicorn adsorbent” and intensified DAC processes. This will improve the performance of solid adsorbent direct air capture and help drive down the costs of this vital technology to avert the worst impacts of climate change.

Creating business value with process mining

Information systems research has a long-standing interest in how organizations gain value through information technology. In this article, we investigate a business process intelligence (BPI) technology that is receiving increasing interest in research and practice: process mining. Process mining uses digital trace data to visualize and measure the performance of business processes in order to inform managerial actions. While process mining has received tremendous uptake in practice, it is unknown how organizations use it to generate business value. We present the results of a multiple case study with key stakeholders from eight internationally operating companies. We identify key features of process mining – data & connectivity, process visualization, and process analytics – and show how they translate into a set of affordances that enable value creation. Specifically, process mining affords (1) perceiving end-to-end process visualizations and performance indicators, (2) sense-making of process-related information, (3) data-driven decision making, and (4) implementing interventions. Value is realized, in turn, in the form of process efficiency, monetary gains, and non-monetary gains, such as customer satisfaction. Our findings have implications for the discourse on IT value creation as we show how process mining constitutes a new class of business intelligence & analytics (BI&A) technology, that enables behavioral visibility and allows organizations to make evidence-based decisions about their business processes.

Prediction of Sulfur Removal from Iron Concentrate Using Column Flotation Froth Features: Comparison of k-Means Clustering, Regression, Backpropagation Neural Network, and Convolutional Neural Network

Froth feature extraction plays a significant role in the monitoring and control of the flotation process. Image-based soft sensors have received a great deal of interest in the flotation process due to their low-cost and non-intrusive properties. This study proposes data-driven soft sensor models based on froth images to predict the key performance indicators of the flotation process. The ability of multiple linear regression (MLR), the backpropagation neural network (BPNN), the k-means clustering algorithm, and the convolutional neural network (CNN) to predict the amount of sulfur removal from iron ore concentrate in the column flotation process was examined. A total of 99 experimental results were used to develop the predictive models. Extracted froth features including color, bubble shape and size, texture, stability, and velocity were used to train the traditional predictive models, whereas in the CNN model the froth images were directly fed into the model. The results comparison indicated that the three-layered feedforward NN model (17-10-1 topology) and CNN model provided better predictions than the MLR and k-means algorithm. The BPNN model displayed a correlation coefficient of 0.97 and a root mean square error of 4.84% between the actual data and network output for both training and the testing datasets. The error percentages of the CNN, BPNN, MLR and k-means models were 10, 11, 15 and 18%, respectively. This study can become a key technical support for the application of intelligent models in the control of the operational variables for the flotation process used to desulfurize iron concentrate.

Bias-Corrected Maximum Likelihood Estimation and Bayesian Inference for the Process Performance Index Using Inverse Gaussian Distribution

In this study, the estimation methods of bias-corrected maximum likelihood (BCML), bootstrap BCML (B-BCML) and Bayesian using Jeffrey’s prior distribution were proposed for the inverse Gaussian distribution with small sample cases to obtain the ML and Bayes estimators of the model parameters and the process performance index based on the lower specification process performance index. Moreover, an approximate confidence interval and the highest posterior density interval of the process performance index were established via the delta and Bayesian inference methods, respectively. To overcome the computational difficulty of sampling from the posterior distribution in Bayesian inference, the Markov chain Monte Carlo approach was used to implement the proposed Bayesian inference procedures. Monte Carlo simulations were conducted to evaluate the performance of the proposed BCML, B-BCML and Bayesian estimation methods. An example of the active repair times for an airborne communication transceiver is used for illustration.

A New Process Performance Index for the Weibull Distribution with a Type-I Hybrid Censoring Scheme

A new life performance index is proposed for evaluating the quality of lifetime products. The maximum likelihood estimation method and the Bayesian approaches using informative and non-informative prior distributions are utilized to infer the parameters of the Weibull distribution and the proposed new life performance index under a Type-I hybrid censoring scheme. Monte Carlo simulation results show that two Bayesian approaches outperform the maximum likelihood estimation method in terms of the measures of relative bias, relative mean square error, and coverage probability for the point and confidence interval estimators, respectively. The Bayesian approach using a non-informative prior distribution is recommended if the knowledge of setting up the hyper-parameters in the informative prior distribution is not available. Two real data sets are provided for illustration.

Manufacturing process planning in aerospace systems

Process planning is a task comprising a broad range of activities to design and develop an appropriate manufacturing process for producing a part. Interpretation of the part design, selection of manufacturing processes, definition of operations, operation sequences, machining datums, geometrical dimensions and tolerances are some common activities associated with the task. Manufacturing process planning can be defined as systematic determination of detailed methods by which work pieces or parts can be manufactured economically and competitively from raw material stage to final finished size. Geometrical features, dimensional sizes, tolerances, materials, and finishes, are analyzed evaluated to determine appropriate sequence of processing operations, which are based on specific, available resources viz machinery and manpower. Inputs to process planning are designing data, raw material data, resource data (machining data, tooling data, fixture data etc), quality requirement data, production type data etc. The output of a process planning is a process document which is an important document in production management. The paper mainly discusses about production planning and control of metallic manufacturing activities in an aerospace Research and Development (R&D) industry, main emphasis on manufacturing process planning. The general process planning methodology adopted in an aircraft industry is discussed. Elements and Functions of process planning, model of process planning, main mission of process planning, the interaction of processes and its process performance indices are discussed and presented. Importance of manufacturing process planning in an aerospace R&D industry is debated. Also, a case study of process planning adopted for manufacturing of a critical Aircraft system component is discussed.