Stream Process Research Articles

Reducing Manufacturing Lead Time With Lean Manufacturing Approach Case Study: CV Suho Garmindo

The purpose of this study is to reduce the Lead Time Manufacturing of mask products at CV Suho Garmindo. At this time it is known that Lead Time Manufacturing is influenced by Non-Value-Added activities and Necessary Non-Value Added activities in which there are several wastes that occur, these activities include the temporary storage process in the loading section, the process of reworking defective products and the transportation process. The method used is Lean Manufacturing, the tools used are Value Stream Mapping (VSM) and Process Cycle Activity (PAM). Value Stream Mapping is used to identify activities that have a cycle time greater than the takt time, followed by Process Cycle Activity mapping to analyze activities that are Non-Value Added activities. Furthermore, NVA activities are analyzed with 5W1H, and 5 why's tool to find out the root of the problem. Based on the research results from 126.1 minutes of lead time process 110 minutes is a non-value added activity. The rework process is identified as having the largest value of contributing lead time, so rework must be eliminated. After it is known that the root cause of the rework process is due to the ineffectiveness of the sewing process (bottleneck occurs), then for improvement the author proposes the needs of the available machines and man power. The changes are the number of machines from 9 to 10, sonday operators from 2 people to 1 person, finished stick operators from 2 people to 1 person, and funnel operators from 2 people to 4 people. Furthermore, the optimal layout improvement based on production flow is described. Due to research limitations, this research did not reach the implementation stage. So with the change in the number of machine requirements and available man power, it is expected that the rework process will be reduced so that the Manufacturing Lead Time can be reduced.

Perspectives on English Language Teaching and Learning during COVID-19 Pandemic

Introduction: Since its outbreak, the COVID-19 pandemic has affected the worldwide education systems, leading to school closures in many countries as well as changes in the whole educational system. Therefore, there has been a rapid shift from face-to-face education to emergency online teaching. English language teaching and learning is an integral flow in this stream of online processes. Hence, this study aimed to explore both teachers’ and students’ perspectives on English language teaching and learning during COVID-19 pandemic.
 Methodology: The participants included 23 English as Foreign Language learners and 18 teachers. The respondents took part in semi-structured interviews and filled out the questionnaire. Thematic analysis was performed to analyze the obtained data.
 Results: The findings from qualitative data revealed that all the participants, as learners, agreed upon the positive influences of online English learning in terms of skill development. Besides, they also considered the flexibility of class schedules and a variety of online assignments as the biggest advantages while bothering with some disadvantages such as internet/technical and health problems. As for English language teachers, speaking skill was the most challenging skill to help students; moreover, some of their concerns included difficulties in class management, online interaction, facilitation, assessment, and digital literacy. Nevertheless, they confirmed the advantages of online resources, new knowledge of technological issues, and development of learner autonomy.
 Conclusion: Overall, the research appeals to more support from university officials to improve the quality of online English teaching and learning. It also contributes to making both teachers’ and learners’ voices heard during the coronavirus quarantine.

VALUE STREAM MAP OPTIMIZATION MODEL IN THE FIELD OF EDUCATIONAL SERVICES

The relevance of the implementation the Lean Production quality management model in higher education institutions (HEIs) in Ukraine has been proven to optimize the stream of HEIs business processes in order to improve the quality of functioning of HEI as an economic activity subject and increase the competitiveness of educational services, scientific and educational products. The modern approaches of scientists to the Lean Higher Education and the construction of a process model of activity and the identification of key business processes of HEIs are analyzed. The purpose of the article is to develop the optimization model for the stream of business processes based on Lean technologies and to apply the model to visualize and optimize the key process of business process at the Kharkiv National University of Radio Electronics (NURE). A DFD model of the optimization process of the Value Stream Map (VMS) - a quality management tool created within the framework of the Lean methodology for process stream analysis - was developed. The model optimization criteria are proposed. During the development of the map optimization model, the peculiarities of the result of the HEI activity, which is a combination of educational service, educational and scientific product, were taken into account. The practical significance of the article lies in the study of the HEI key process "Development of educational and methodological support of the educational process". Decomposition of the main process was carried out. The second and third levels processes for the value stream mapping was distinguished. The current state VSM of the process "Development of educational and methodological support of the educational process" was developed, the indicators of the effectiveness of the value stream at the department of information and measurement technologies of the NURE were calculated, the "bottlenecks" of the stream were analyzed. Measures to optimize "bottlenecks" based on the criterion of minimizing time are proposed. Prospects for further research in the chosen direction are determined as the implementation of Lean technologies in the activities of HEIs are the development and optimization of VSM for all key processes of HEIs.

Process simulation for technical and economic evaluation of acrolein and glycerol carbonate production from glycerol

The objective of this work is the technical and economic evaluation of acrolein and glycerol carbonate production from glycerol, byproduct of biodiesel production. The processes were simulated in Aspen HYSYS® v8.8 and the capital and production costs were estimated. Among the two analyzed processes, only glycerol carbonate production was economically viable in the base scenario, due to the high reactivity of the selected catalyst and the simplicity of the reaction and separation steps. In addition to requiring a more complex separation system, the high degree of dilution of the inlet stream of the acrolein production process entails high costs for some equipment, as well as significant expenditure on utilities and effluent disposal. Such characteristics raise both the total investment and the production cost to a degree which makes the process economically unfeasible. However, it was shown that crude glycerol processing and further reaction development could make it also possible.

Glutamate as a non-conventional substrate for high production of the recombinant protein in Escherichia coli.

The economic viability of the biomass-based biorefinery is readily acknowledged by implementation of a cascade process that produces value-added products such as enzymes prior to biofuels. Proteins from the waste stream of biorefinery processes generally contain glutamate (Glu) in abundance. Accordingly, this study was initiated to explore the potential of Glu for production of recombinant proteins in Escherichia coli. The approach was first adopted by expression of D-hydantoinase (HDT) in commercially-available BL21(DE3) strain. Equipped with the mutant gltS (gltS*), the strain grown on Glu produced the maximum HDT as compared to the counterpart on glucose, glycerol, or acetate. The Glu-based production scheme was subsequently reprogrammed based on the L-arabinose-regulated T7 expression system. The strain with gltS* was further engineered by rewiring metabolic pathways. With low ammonium, the resulting strain produced 1.63-fold more HDT. The result indicates that Glu can serve as a carbon and nitrogen source. Overall, our proposed approach may open up a new avenue for the enzyme biorefinery platform based on Glu.

The Split Flow Process of CO2 Capture with Aqueous Ammonia Using the eNRTL Model

Carbon Capture and Storage (CCS) technology has attracted increasing attention as global climate change accelerates. Carbon dioxide removal processes under development include pressure swing adsorption (PSA) and chemical absorption using amine solvents. In this paper, an ammonia solvent, which is relatively inexpensive and has good material properties, was used instead of amines in the carbon dioxide removal process simulation as a chemical absorption method. This simulation used the eNRTL thermodynamics model which has the advantage of predicting ions in the liquid phase in Aspen Plus. A case study (Case Study 1) was conducted to verify the validity of the thermodynamic model. The purpose of this research was to find the operating conditions to eliminate more than 90% of the carbon dioxide contained in the flue gas from coal-fired power stations, and to lower heat duty and operating cost conditions. A second case study (Case Study 2) was conducted to find the operating conditions by comparing various process operating conditions. Additionally, this paper determined lower operating cost conditions by manipulating the amount of steam and cooling water. The results showed that the heater’s outlet temperature should be set at under 80 ℃ to lower the operating costs. As a result of changing the flow rate of the side stream of the split flow process, energy consumption was reduced when compared to the conventional flow process. It was shown that the split flow is a superior process with 10.24% less energy use than the conventional flow. In this study, the split flow process achieved an energy saving advantage when compared to the conventional flow process, and a carbon dioxide removal rate of 95% was achieved.

Effects of Particle Size Distribution and Impact Speed on Printing Quality in Direct Energy Deposition

Particle Size Distribution (PSD) has been studied as an important factor that will affect printing quality in selective laser sintering/melting (SLS/SLM). However, few studies investigated the effects of particle size distribution and associated impact speeds on the quality of printed surfaces in Direct Energy Deposition (DED) processes. Due to the different characteristics of powder bed and powder fed printing mechanism, particle size distribution could be evaluated before the printing but could not be assessed in-situ during the printing process of SLS/SLM. The powder stream of the DED process provides the opportunity to evaluate the PSD in-situ using a novel laser diffraction method. The laser diffraction method was a well-established particle size distribution analysis method for soil granite. It can achieve a fast, accurate, and in-situ analysis but has never been studied in DED printing. In this study, a novel laser diffraction PSD analyzer was proposed. A high-speed camera was applied to investigate the relationship between the impact speed and carrier gas flux. Stainless steel 316L metal powders with different PSDs and impact speeds were DED printed, the surface roughness and tensile strength were measured to evaluate the effects of impact speeds and PSDs on the printing quality. The results showed that impact speed and PSD had considerable influences on the printed surface roughness and tensile strength of printed parts. This study paves the way for adopting laser diffraction as an in-situ monitoring tool for the DED processes.

On the usage of online fiber measurements for predicting bleached eucalyptus kraft pulp tensile index — an industrial case

Cellulose pulp’s physical-mechanical properties are determined by laboratory tests obtained from prepared handsheets. However, this procedure is time intensive and presents a lead time until the results are available, hindering its utilization for monitoring and decision-making in a pulp mill. In this context, developing real-time solutions for physical-mechanical properties prediction is fundamental. This work applied a mathematical modeling approach to develop a soft sensor for tensile index monitoring. The mathematical model considers online morphology measurements obtained from the last bleaching stage outlet stream and important process variables for tensile index prediction. The results obtained are satisfactory compared to laboratory results, presenting a mean absolute percentual error of 2.5%, which agrees with the laboratory testing method’s reproducibility.

Total physical response (TPR)

This paper aims at the effective utilization of TPR approaches in teaching English as a second language among children with dyslexia. This stress free simple teaching strategy is developed by James Asher. It emphasizes at bringing the dyslexics to the main stream of learning process. As T.P.R consists of different easy and fun activities it helps the student to understand the target language very effectively. It raises the pace and mood of the student. This method is very beneficial for kinaesthetic learners who need to be energetic in class. It involves the left and right brained learning. This approach is considered to be a fruitful method to help language learners to recall, accumulate, use and regain words in long term memory. To obtain a clear understanding on the efficacious of TPR teaching method among dyslexic students, a survey is conducted among teachers who teach dyslexic students. The questionnaire includes 10 questions. While analysing the survey, the responses from the teachers indicates that the students respond to the imperative drills and other activities very actively. So adopting TPR as a teaching strategy helps the children with dyslexia to learn English more effectively in a stress free environment.

Conversion of carbohydrates to carboxylic acids during hydrothermal and oxidative treatment of concentrated kraft black liquor

Black liquor, a side stream of the kraft pulping process, contains valuable low molecular weight carboxylic acids and carbohydrates. Hydrothermal treatment and wet oxidation of black liquor with a dry matter content of 43 % were investigated as an approach to convert these carbohydrates to carboxylic acids to increase their concentration. Wet oxidation with H2O2 or O2 at 115–185 °C led to partial degradation of carbohydrates, but no significant formation of the investigated carboxylic acids, glycolic, lactic, formic and acetic acid, was detected. Treatment under N2 atmosphere at 185 and 220 °C finally led to an increase of the hydroxy acid concentration. After two hours of heat treatment at 220 °C, 90 % of the carbohydrates were degraded, coupled with a high carbon conversion efficiency of 32 % based on the formation of lactic acid and glycolic acid, of which the concentrations increased by 51 and 73 %, respectively.

Neural network based control charting for multiple stream processes with an application to HVAC systems in passenger railway vehicles

AbstractA multiple stream process (MSP) is a process at a point in time that generates several streams of output with a quality variable of interest and specifications that are identical in all streams. In this article, a new control charting framework based on artificial neural networks (NNs), whose performance is prone to be measured in terms of and , is proposed to improve the monitoring of a MSP and the detection of changes in individual streams. To the best of our knowledge, this is the first time that a NN has been applied to the monitoring of a MSP. The performance of the proposed control charting is evaluated through a wide Monte Carlo simulation and is compared with the traditional Mortell and Runger's MSP control charts based on the range statistic. The proposed method's potential is demonstrated by means of a real‐case study in the monitoring of heating, ventilation and air conditioning (HVAC) systems installed on board modern trains. TheNN4MSPpackage that implements the proposed monitoring scheme through the software environmentPython, and theHVACdata set are made openly available online athttps://github.com/unina‐sfere/NN4MSPand on PyPI, together with a tutorial that shows how to practically implement the proposed methodology to the real‐case study.

Investigation of Coal Preparation for Life Cycle by Using Building Information Modeling (BIM): A Case Study

Increasing the efficiency of coal preparation is the key link of mine production. This study proposed a framework of coal preparation for life cycle based on Building Information Modeling (BIM), in which the data resources can be retransmitted, and the integration of BIM application links has a high level. In this paper, the kappa big data processing architecture is used to realize the integration and unification of stream data and batch data processing process. By using deep learning method and multimodal data fusion method, the multimodal data association fusion is realized, and Bentley software is adopted for verification and integration. Taking a coal preparation plant as an engineering case study, the proposed framework is applied to build a big data subplatform of three-dimensional visual management. The following is suggested: (1) the three-dimensional visual review of the coal preparation online is realized, and the efficiency of the design results is improved; (2) the production and operation management of the coal preparation are carried out through the established platform, which indicated that the proportion of clean coal increased by 1.97%, and the average increase percentage for the total output is about 5.6%. Results from the case study show that the framework proposed in this study can provide efficient reference for coal washing in the coal preparation.

Cancer Clinic Redesign: Opportunities for Resource Optimization.

Ambulatory cancer centers face a fluctuating patient demand and deploy specialized personnel who have variable availability. This undermines operational stability through the misalignment of resources to patient needs, resulting in overscheduled clinics, budget deficits, and wait times exceeding provincial targets. We describe the deployment of a Learning Health System framework for operational improvements within the entire ambulatory center. Known methods of value stream mapping, operations research and statistical process control were applied to achieve organizational high performance that is data-informed, agile and adaptive. We transitioned from a fixed template model by an individual physician to a caseload management by disease site model that is realigned quarterly. We adapted a block schedule model for the ambulatory oncology clinic to align the regional demand for specialized services with optimized human and physical resources. We demonstrated an improved utilization of clinical space, increased weekly consistency and improved distribution of activity across the workweek. The increased value, represented as the ratio of monthly encounters per nursing worked hours, and the increased percentage of services delivered by full-time nurses were benefits realized in our cancer system. The creation of a data-informed demand capacity model enables the application of predictive analytics and business intelligence tools that will further enhance clinical responsiveness.

Tolerance Limit-based Estimation of the Proportion of Non-conforming Parts in a Multiple Stream Process

The conventional way to characterize the proportion of non-conforming parts in a process is to calculate process capability indices and transform them into a ratio. These widely used indices are able to give digestible information about the ratio of non-conforming parts if some assumptions are fulfilled. A correct estimation method should be based on the output distribution of the process, and the uncertainty of the parameter estimates should be considered, as well. In this article, a special case of the output distribution is examined: a mixture of normal distributions is considered. In practice, this output distribution appears if a multiple stream process is investigated. The novelty of this study is to apply the tolerance interval-based estimation method for the proportion of non-conforming parts in a case study of a multiple stream process and to qualify the limitations of the proposed estimation method. A simulation study is performed to investigate the bias, mean square error, and root mean square error of the estimates from the two estimation methods (process performance index-based and tolerance interval-based) for different sample sizes for each stream (N ). It was found that, if it may be assumed that the speed of the streams is equal in the case of the sample sizes investigated (N = 25, 50, 100 per head), the proposed (tolerance interval-based) method overestimates the proportion of non-conforming parts while the conventional (process performance index-based) method underestimates it. The tolerance-limit based estimation method has asymptotically better properties than the process performance index-based estimation method.

Optimalisasi Sistem Antrian di Farmasi Rawat Jalan Rumah Sakit Grha Permata Ibu dengan Metode Lean Hospital

Grha Permata Ibu Hospital is one of the major hospitals in Depok with adequate facilities in its services. One of the main factor in hospital services is drug serviceat the pharmacy. Often the problema in drug service is the long waiting time from drug provision, therefore this research was conducted to identify the waste that occurs in drug services in outpatient pharmacies using the Lean Hospital method by making Value Stream Mapping (VSM) and Process Activity Mapping (PAM). This research was also conducted using quantitative research method to see the optimization in the queuing system in drug services in outpatient pharmacies. Meanwhile the resulto f the observation and calculation of the queuing system in drug services at the pharmacy are that the speed of prescription arrival is 0.297 minutes, for the calculation of the speed of prscription sevice is 3.366 minute per patient, the expected waiting time in the system is 0.32 minutes per patient waiting on the queue is 0.029 minutes per patient. Base on the result of the percentage of Process Cycle Efficiency (PCE), it is known that the efficiency of activity time in drug services in outpatient pharmacies has increased 10.8% from the eclusive system. The resulto f these observations made proposal for improvement in drug services in outpatient pharmacies

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.

Mathematical investigation of two-stage dehumidification from single rotary dehumidifier with and without precooling

A one-dimension mathematical model has been developed to investigate the performance of two-stage dehumidification using a single rotary dehumidifier of four sectors. To obtain two-stage dehumidification, the outlet stream of process 1 has been supplied into process 2 and a precooler is also introduced to enhance the performance. The performance of two-stage dehumidification using four sectors with and without precooling has been compared with the single-stage dehumidification using two-sector. The results of this study indicate that the total moisture removal of two-stage-dehumidifier using four sectors with precooling is significantly higher than single-stage-dehumidifier using two sectors, but only a little higher than the Two-stage dehumidifier using four sectors without precooling. The introduction of a precooler not only decreases the exit temperature but also increases the total moisture removal. Hence, this study recommends that a two-stage dehumidifier using four sectors can be used for better performance in place of a single-stage dehumidifier using a two-sector for the same size or space of the wheel in a variety of applications.

Membrane and Electrochemical Based Technologies for the Decontamination of Exploitable Streams Produced by Thermochemical Processing of Contaminated Biomass

Phytoremediation is an emerging concept for contaminated soil restoration via the use of resilient plants that can absorb soil contaminants. The harvested contaminated biomass can be thermochemically converted to energy carriers/chemicals, linking soil decontamination with biomass-to-energy and aligning with circular economy principles. Two thermochemical conversion steps of contaminated biomass, both used for contaminated biomass treatment/exploitation, are considered: Supercritical Water Gasification and Fast Pyrolysis. For the former, the vast majority of contaminants are transferred into liquid and gaseous effluents, and thus the application of purification steps is necessary prior to further processing. In Fast Pyrolysis, contaminants are mainly retained in the solid phase, but a part appears in the liquid phase due to fine solids entrainment. Contaminants include heavy metals, particulate matter, and hydrogen sulfide. The purified streams allow the in-process re-use of water for the Super Critical Water Gasification, the sulfur-free catalytic conversion of the fuel-rich gaseous stream of the same process into liquid fuels and recovery of an exploitable bio-oil rich stream from the Fast Pyrolysis. Considering the fundamental importance of purification/decontamination to exploit the aforementioned streams in an integrated context, a review of available such technologies is conducted, and options are shortlisted. Technologies of choice include polymeric-based membrane gas absorption for desulfurization, electrooxidation/electrocoagulation for the liquid product of Supercritical Water Gasification and microfiltration via ceramic membranes for fine solids removal from the Fast Pyrolysis bio-oil. Challenges, risks, and suitable strategies to implement these options in the context of biomass-to-energy conversion are discussed and recommendations are made.

An Exact Method for a Class of Self-Similar M/Ga/1/K Queueing System of Internet Stream

In Internet stream monitoring, computing the expected parameters and distributions for the stream process under self-similarity and long-range dependency requires appropriate and exact methods queueing analyses to obtain performance of the Internet. Our method avoids fitting a non-heavy tailed distribution to Internet stream data by specifically fitting Gamma distribution to its service time process, which enhances the features of correlated events that explain self-similarity and long-range dependency compared to the classical memoryless assumptions of standard Poisson arrival and Exponential service. We propose the specific expectations of the parameters for a class of M/Ga/1/k queueing system (‘M’ represents a Markovian arrival process that follows Poisson distribution, ‘Ga’ represents a Gamma distribution service process, ‘1’ represents a single server, ‘k’ represents the buffer size) in an exact closed-form derivations using the Pollaczek Khintchine formulae alongside the Laplace transform to obtain the performance of queue. The adequacy of the proposed model was confirmed using simulated data of Internet traffic. Keywords- Gamma distribution, Internet stream, M/Ga/1/K model, Self-similarity, heavy-tail distribution

Remarkable NH3 absorption in metal‐based deep eutectic solvents by multiple coordination and hydrogen‐bond interaction

AbstractThe elimination and recycling of NH3 from the vented circulating stream of NH3 synthesis process is of great significance to the protection of air environment and saving of NH3 product. Deep eutectic solvents (DESs) with as many weak acidic sites as possible are anticipated to exhibit high efficiency for NH3 absorption at low pressures. Herein, we designed and prepared a series of metal‐based DESs by mixing EaCl, metal chloride (SnCl2, ZnCl2, FeCl3, or CoCl2), and Gly. The physiochemical properties, NH3 absorption performance, and mechanism of prepared metal‐based DESs were investigated systematically. It is found that considerably high solubilities of NH3 in DESs can be achieved by utilizing the multiple coordination and hydrogen‐bond interaction with NH3 provided by metal ions and Gly respectively. The values of NH3 solubilities (17.55 mol/kg at 298.2 K and 103.0 kPa, and 10.24 mol/kg at 298.2 K and 6.8 kPa) are the highest reported to date.