Production Process Control Research Articles

Ginkgo biloba leaf extract EGb 761® as a paragon of the product by process concept.

It is an often-neglected fact that extracts derived from the very same plant can differ significantly in their phytochemical composition, and thus also in their pharmacokinetic and pharmacodynamic properties which are the basis for their clinical efficacy and safety. The Ginkgo biloba L. [Ginkgoaceae] special extract EGb 761® is one of the best-studied plant extracts in the world. In the present review, using that extract as a paradigm, we describe insights how climate, the harvest region, processing of the plant material, the drying process, the extraction solvents, and the details of the subsequent process steps substantially impact the quality and uniformity of the final extract. We highlight the importance of regulating active constituent levels and consistent reduction of undesired substances in herbal extracts. This is accomplished by a controlled production process and corresponding analytical specifications. In conclusion, since extracts derived from the same plant can have very different phytochemical compositions, results from pharmacological, toxicological and clinical studies gained with one specific extract cannot be extrapolated to other extracts that were generated using different production processes. We propose that the heterogenous nature of extracts should be meticulously considered when evaluating the efficacy and safety of plant-derived remedies.

A homodimeric IL-15 superagonist F4RLI with easy preparation, improved half-life, and potent antitumor activities.

Interleukin-15 (IL-15) is a promising candidate for cancer immunotherapy due to its potent immune-activating effects. There are several IL-15 molecules currently in clinical trials but facing shortages of poor half-life, circulation instability, or complicated production and quality control processes. The aim of this study is to design a novel IL-15 superagonist to set out the above difficulties, and we constructed F4RLI consisting of the GS-linker spaced IgG4 Fc fragment, soluble IL-15 Rα (sIL-15Rα), and IL-15(N72D). Using a single plasmid transient transfection in HEK293E cells, the matured F4RLI was secreted in the form of homodimer and got purified by an easy step of protein A affinity chromatography. The F4RLI product can significantly stimulate the proliferation of human CD3+CD8+ T cells and NK cells in vitro. Meanwhile, F4RLI greatly extended the half-life and prolonged the exposure of IL-15 in mice nearly by 28- and 200-fold, respectively, in comparison with that of the IL-15 monomer. In vivo, F4RLI vastly expanded mouse splenic CD8+ T lymphocytes, illustrating its potential in tumor immunotherapy. Further studies showed that the combination of F4RLI with the immune checkpoint blocker atezolizumab played a synergistic effect in treating MC38 mouse tumor by increasing the percentage of CD8+ T cells in tumor tissue. Moreover, the combination therapy of F4RLI with the angiogenesis inhibitor bevacizumab resulted in significant tumor growth suppression in a xenograft human HT-29 mouse model. Overall, our results demonstrate a homodimeric IL-15 superagonist F4RLI with advances in manufacturing processes and biopharmaceutical applications for cancer immunotherapy. KEY POINTS: • The homodimeric structure of F4RLI facilitates its easy production processes and quality control. • The fusion with Fc and sIL-15Rα extends the plasma half-life of IL-15 by about 28-fold. • F4RLI can play synergistic antitumor activity with the PD-1/PD-L1 checkpoint inhibitor or angiogenesis inhibitor.

Efficient Detection of Pre-Proinsulin by Double Antibody Sandwich ELISA

To detect residual pre-proinsulin (PPI) in recombinant human insulin production, an analytical method based on double-antibody sandwich ELISA was developed in this study. The BALB/c mice were immunized with PPI, and the hybridomas secreting anti-PPI monoclonal antibodies were obtained using the conventional cell fusion technique and ELISA screening. We purified the antibody using a Protein G gel column and identified its purity by SDS-PAGE. The sandwich ELISA was used to explore the pairing effect, and the specificity of the paired antibody was determined. We selected a paired antibody with relatively good specificity to establish sandwich ELISA, constructed a quantitative curve, and evaluated the accuracy and sensitivity of the method. Six anti-PPI monoclonal antibodies were obtained, named P1, P2, P3, P4, P5 and P6, of which P5 had the highest titer value. The sandwich ELISA method was established with P5 for plating and P2 as detection antibodies. The linear range of the quantitative curve of PPI by sandwich ELISA was 0.645 to 82.5 pg/mL, the recovery was 95% and the detection limit was 3.06 pg/mL. In this study, we prepared six anti-PPI monoclonal antibodies and established the sandwich ELISA method to detect PPI in process control and product release control for recombinant human insulin production.

A new approach for quality prediction and control of multistage production and manufacturing process based on Big Data analysis and Neural Networks

As consumers care more and more about product quality, it is important to mine the deep correlations between production and manufacturing parameters and the evaluation of product quality through the analysis of industrial big data. The existing research of product quality prediction faces several major problems: the lack of diverse quality features, the poor tractability of abnormal parameters, the strong nonlinearity of parameters, the obvious sequential property of data, and the severe time lag of data. To solve these problems, this paper explores the quality prediction and control of multistage MP process (MPMP) based on big data analysis. Firstly, the prediction strategy and flow were specified for MPMP product quality prediction, and the features were extracted from MPMP product quality. After that, the MPMP product quality features were described in multiple dimensions, the attention mechanism was introduced to the prediction process. In addition, the recurrent neural network was improved, and an MPMP product quality prediction model was established on bidirectional long short-term memory (BiLSTM) network. Our model was compared with AdaBoost and XGBoost through experiments. The effectiveness of our model was demonstrated by the results of the appearance quality PQ1, and the area under the curve (AUC) for each process parameter. In general, our model is superior to other algorithms in the accuracy, mean accuracy, and precision of product quality prediction.

Efficiency of joint glycine and auxin analogs foliar treatment on coriander seeds yield and essential oil quality

Coriander is the leading essential oil crop and occupies vast areas, both in our country and abroad, and is be used to produce linalool, with subsequent processing into other aromatic substances. Increasing its yield and quality of raw materials with the help of modern preparations is an urgent problem of essential oil production.Purpose of the work: increasing the productivity of coriander with combined use of foliar treatments with glycine and auxin-containing preparations for directed control production process in coriander seed.Material and methods. As objects for studying the effect of the drug, varieties of coriander sowing Yantar and Avangard were chosen. Plants were sown in the first or third decade of April, depending on the conditions of the year, using a SZT-3.6 seeder with row spacing of 15 cm. The seeding rate was 25 kg/ha, the seeding depth was 2 cm. concentration 10 mg/l. Drug concentrations: IAA-glycol phosphate – 25, 50 and 100 mg/l, DvaU - 2 ml/l. Treatment with a solution of IUKGF and DvaU was carried out in the phase of budding-beginning of flowering. The cutting was carried out during the period of browning of seeds on the central umbrella. The content of essential oil was determined according to SP XIV method 1.Results. As a result of the research, a positive effect of foliar treatments with glycine was revealed, both on the yield and on the content of essential oil in the raw material of coriander seed varieties Yantar and Avangard. Based on the results obtained, the effective concentration of the drug is determined not only by the characteristics of the variety, but also by weather conditions, when, depending on the conditions during the processing period and the previous harvest, different aspects of the drug action appear. The maximum increase in the collection of essential oil of coriander variety Yantar (11.1 kg/ha) was noted during the combined treatment of plants with glycine at a rate of 10 mg/l in the leaf rosette phase and IAA-GF at a rate of 50 mg/l in the budding phase; varieties Avangard (8.6 kg/ha) - with combined treatment of plants with glycine at a rate of 10 mg/l in the leaf rosette phase and DvaU at a rate of 2 ml/l in the budding phase.

Integrated deep learning-production planning-economic model predictive control framework for large-scale processes. A fluid catalytic cracker-fractionator case study

In this paper, we seek a tighter coordination between the production planning and process control layers in the process decision-making hierarchy, with the purpose of reducing the economic gap between planned and realized production. We introduce deep learning-based frameworks that support a stochastic production planning (PP) formulation that integrates economic model predictive control (EMPC) and PP via a feedback mechanism that relays the effect of process disturbances (traditionally dealt with by the control layer) to the planning layer.Our framework demonstrates superior economic performance, promising solution times (potentially allowing industrial implementation) and a close match of planned and realized process economics compared to a traditional industrial linear and non-integrated EMPC-PP benchmark.

A New Dynamic and Perspective Parsimonious AHP Model for Improving Industrial Frameworks

Multi-criteria decision methods (MCDMs) are used as an effective tool to support decision makers (DMs) in critical decision processes. These methods are used in several fields of application by analyzing static decision-making problems in which it is assumed that the decision is made at a precise moment. By increasing the complexity of decision-making problems and operating in increasingly competitive production sectors, very often analyzing a decision-making problem in a static way is not enough. This paper deals with considering the temporal variable in the construction of a dynamic MCDM, which takes into account historical and current data in order to learn from the past; and prospective also allowing to have a forecasting perspective of future data through the use of techniques that work in this sense. Our approach was tested in a multinational company in the manufacturing sector. The results show that the use of dynamic approaches allows DMs to obtain more precise alternative rankings given the information they exploit from the past; furthermore, the use of the prospective model, integrated with the dynamic one, makes it possible to provide greater detail on the possible future rankings of the alternatives that update their positions based on the feedback received. The approach allows for drawing advantages from a management point of view as it defines a complete decision support tool for the choices related to the planning and control of production processes. Our approach can be implemented in corporate information systems. Furthermore, the involvement of the DM in the construction of the model helps to define a learning process that feeds the decision-making process by generating greater awareness of the DM on the choices to be made.

Application of mathematical modelling methods in oil production management

Purpose. To assess the effectiveness of the practical application of mathematical modelling methods of the operation principles of oil production management systems in a fuzzy environment and the main aspects of their application in oil production. Methodology. The basis of the methodological approach in this study is a combination of methods of quantitative and qualitative analysis of the basic principles of the development of mathematical models for managing oil production processes in a fuzzy environment. Findings. The authors investigated the available methods of mathematical modeling in order to select the optimal possibilities for creating mathematical models. A qualitative assessment of the effectiveness of the practical application of methods of mathematical modeling of the principles of operation of oil production management systems in a fuzzy environment was formulated. The main aspects of application of methods of mathematical modeling directly in the process of oil production were established. Originality. A fuzzy approach is proposed for solving the problem of multi-criteria optimization in the development of a field and oil production, in which the task is set and solved in a fuzzy environment without previously converting them to equivalent clear tasks. This ensures the correctness and efficiency of the solution by increasing the adequacy of the description of the problem in a fuzzy environment. Practical value. The results obtained and the conclusions formulated on their basis are of considerable practical importance for employees of research institutes of the oil industry responsible for the development of effective methods of mathematical modelling of oil production process control systems and for employees of oil companies whose professional duties include the introduction of the mentioned mathematical models in oil fields.

Peningkatan Kualitas Produk Minuman IKM melalui Pengendalian Proses Produksi dan Higienitas

Small and Medium Enterprises (SMEs) are the largest business entities in Indonesia. Of this very large number, the largest proportion of SMEs is engaged in food and beverage production. Unfortunately, with such a large number, the awareness to apply food safety and hygiene standards in the SME production process is still considered very low. The results of our study found that one of the factors that affect product quality and competitiveness of SME is production process control and hygiene. The results of this research were then applied to a community service program for beverage SMEs in Malang Regency in the form of training on good and hygiene processed food production methods. The results of statistical tests and further observations carried out on the training participants found that this community service program was effective in increasing SMEs knowledge about good production methods, hygiene, and sanitation as well as bringing positive changes to their production systems which are expected to further improve product quality and competitiveness of SMEs.

Sliding window and dual-channel CNN (SWDC-CNN): A novel method for synchronous prediction of coal and electricity consumption in cement calcination process

As a representative of the process industry, the cement industry consumes a large amount of coal and electricity resources. This is mainly due to the irrational energy scheduling caused by the rough production and independent statistics of each energy consumption indicator within the cement industry. The synchronous accurate prediction of energy consumption can provide a more resultful scheme for the production control process and energy scheduling. However, due to the time delay, variables coupling, and uncertainty of production, it is difficult to synchronously forecast multiple indicators. In this paper, a data-driven prediction method combining Sliding Window and Dual-Channel Convolutional Neural Network (SWDC-CNN) is proposed to achieve synchronous prediction of coal and electricity consumption in the next 5 min. The sliding window method is designed to extract time-varying delay characteristics of time series data to overcome its influence on energy consumption prediction. The effect of redundant parameters between weakly correlated variables on energy prediction is reduced by designing a dual-channel structure. We experimented and compared with excellent models Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gate Recurrent Unit (GRU) on an actual cement production data in Shanxi Province, China. Experimental results show that the proposed SWDC-CNN model has good performance, the highest prediction accuracy, and can meet the expected requirements.

Manajemen Strategik Pemasaran Perusahaan Jasa Alih Daya PT. Pelita Adhidaya Servindo

In this digital era, business between countries has entered the virtual network. The business climate is highly competitive in almost all sectors due to technological advances. To run a business people are required to manage the business in such a way as to be effective, efficient, and productive super- responsive and flexible to improve service to consumers. Taking into account the complexities of business management, the range of control of production processes and the availability of resources, encourages companies to invite other companies to partner in outsourcing format. By outsourcing a company submitssome of its production process to another company with the aim of obtaining one of the most efficient combination of resource use. In Indonesia the practice of outsourcing is actually not something new. The practice of handing over work to other companies has been known since Dutch colonial times. In Article 1601b of the Civil Code or Burgerlijk Wetboek (BW) it is found that there is a definition of chatering job . It is said that the Contract of Work, is an agreement between one party and another, where the one party (the employer of the work) wants a work which the other party can afford, on the payment of a certain money as the price of the contract.

Development of Liquid Crystal Layer Thickness and Refractive Index Measurement Methods for Scattering Type Liquid Crystal Displays

Abstract We report the measuring method of scattering type display liquid crystal layer thickness based on capacitance values suitable for inline production process control. The method is selected for its effectiveness and simplicity over spectroscopic methods as conventional methods for scattering type displays are not applicable. During the method approbation process, a novel diffuser liquid crystal mixture refractive index was determined based on liquid crystal layer thickness measurement data.

STUDY OF THE HEAT ENGINEERING SCHEMES OF THE OK-520 FIRING MACHINES AT MIKHAILOVSKY GOK USING VNIIMT’S METHODOLOGY TO IMPROVE THEIR EFFICIENCY

Optimization of the heat engineering schemes for thermal treatment of iron-ore pellets is of great importance for increasing productivity of the firing conveyor machines and reducing specific fuel and power consumption. The paper presents a description of methodology of complex multistage research of heat engineering schemes of firing machines developed by JSC VNIIMT, which was used in the development of technical specifications, as well as in the projects for modernization of firing machines and for pellet production process control system at different ore mining and processing plants. The technical solutions are considered; their application provides the maximum effect during modernization of the existing and construction of the new units. The principles of minimization of specific power consumption are established based on comprehensive analysis of heat engineering schemes, heat engineering calculations using mathematical models and firing machines inspection; their realization allows improving technical and economic indices of firing machines operation. The technical characteristics of firing machines were given, the reserves for increasing their performance were shown. The results of the analysis of design and existing heat engineering schemes of firing machines OK-520 at Mikhailovsky GOK are presented. It was established that both designed and existing schemes have reserves for improvement of their operation indicators. Basic technical solutions were determined, and their implementation would improve technical and economic performance of the firing machines.

Plantwide control of a biodiesel production process with variable feedstock

Biodiesel can be obtained from a variety of oils and fats rich in triglycerides. Due to this variability, production processes must be flexible enough to handle different raw materials. This work reports the simulation-based design of a biodiesel production process able to obtain the biofuel from different raw materials. This approach avoids dependence on a single kind of raw material by developing a flexible process. The process is designed to ensure that the product accomplishes the international standard EN 14214 for any of the tested raw materials. A plantwide control structure is then developed to handle the potential variations in feed composition. The controllers’ parameters are tuned, and the control scheme is tested to determine its capability to reach a new steady state in an acceptable period. According to the results, the steady state design allows producing biodiesel from three different raw materials in a single process by only adjusting the operational conditions, obtaining the lowest total annual cost when processing microalgae oil. Moreover, the proposed control strategy allows rejecting the perturbations occurring when the change of raw material occurs, stabilizing after a maximum period of 8 h while maintaining a product still accomplishing the international standard EN 14214.

ASSESSMENT OF TUYIL PARACETAMOL PRODUCTION PROCESS: A STATISTICAL QUALITY CONTROL TECHNIQUES

This research deals with the application of statistical quality control techniques on process control of paracetamol production of Tuyil Pharmaceutical Company with the aim of estimating the control limits and investigate whether the production process is under statistical control. The data used in this research was collected on two measurable quality characteristics of content and weight of paracetamol from 2015-2018. The Mean, Range and EWMA-chart together with process capability analysis were employed for measuring the production process. The result obtained from the analysis on the content of paracetamol, it was observed that for X-bar and R-Chart one point from the observations is out of the control limits while for the EWMA chart all the observations are within control and capable of improving the production process. The analysis also reveals that on the weight of paracetamol, the Mean, Range and EWMA-chart together with process capability analysis are within control and capable of improving the process of production. it was concluded that for the weight of paracetamol is statistically stable and under control while that of the content is partially stable as one point from the observations fall outside the control limits and this could be due to a small shift in the production process which is caused by chance causes of variation such as temperature, room vibration and so on. It is recommended that the company needs to take proper caution so as to improve their production process in order to meet up with consumer’s requirement.

Evaluasi penerapan sanitasi pada proses pengolahan produk manisan salak pondoh di UMKM Sarisa Merapi Sleman, Yogyakarta


 Sarisa Merapi is a micro, small and medium enterprise (MSME) engaged in food processing such as candied snakefruit, snakefruit's juice, snakefruit's lunkhead, telang tea, and eggroll. Among these products, candied snakefruit is the most demanded product due to its sweet and fresh taste, and cheap. The purpose of the study was to evaluate the production process, sanitation application, and quality control of candied snakefruit products in Sarisa Merapi. Data were collected through observation, interviews, literature study and documentation focused on preparing raw materials, processing and packaging. The production process of candied snakefruit includes sorting, stripping, cutting, soaking, washing, draining, boiling, draining, and packaging and storage. The observations found that the application of sanitation and hygiene in the production of candied snakefruit is quite good. However, some problems such as the condition of walls and floors were difficult to clean, workers did not routinely use aprons, and the production room did not have wall partitions to separate the spaces. These problems need to be addressed to minimize the negative impact on the products produced and also on consumers.

The role of bioprocess systems engineering in extracting chemicals and energy from microalgae

Abstract In this study, the role of process systems engineering in enhancing the algae economy is highlighted. First, basic characteristics of the various strains of microalgae are presented. In addition, the beneficial extracted bioproducts and their applications are reviewed. Then, an overview of the various technologies available in each step of biorefinery to produce added-value products and biofuels from microalgae is provided. These technologies are compared in terms of required energy and efficiency. Different perspectives of the algae industry, from molecule to enterprises scale where process systems engineering can have a role, are addressed. Subsequently, the roles of process systems engineering in process and product design, process control, and supply chain of the algae biorefinery are discussed. It is found that process systems engineering can play an important role in the biobased economy, especially by applying sustainability and economic concepts in the decision-making process for selecting the best feedstock, processing pathways, and desired products. Tools such as market analysis, techno-economic analysis, life cycle assessment (LCA), and supply chain (SC) analysis can be applied to design sustainable algae biorefinery. There are, however, several challenges such as the lack of data, the complexity of optimization, and validation that should be addressed before using these tools.

Behavioral Determinants of Routine Health Information System Data Use in Senegal: A Qualitative Inquiry Based on the Integrated Behavioral Model.

Routine health information system (RHIS) data are essential in driving decision making and planning in health systems as well as health programs. However, despite their importance, these data are underutilized, and the underlying individual-level facilitators and barriers to use remain understudied. In this research, we applied the Integrated Behavior Model (IBM) to examine how attitudes toward RHIS data, perceived norms concerning RHIS data use, and the ability to use RHIS data influence the demand and use of RHIS data among stakeholders in Senegal. Using data from interviews with respondents working at national levels of malaria, HIV, and TB control programs in Senegal, we used a framework analysis approach to apply the IBM behavioral constructs and identify their linkages to RHIS data use. We found that attitudes about the quality, availability, and relevance of RHIS data for decision making were important in driving data use among respondents. Institutional expectations, organizational protocols, policies, and practices around RHIS data ultimately shape social norms around the use of the data. Although we found that perceived ability and self-efficacy to use RHIS data were not barriers to RHIS data use among stakeholders at the strategic levels of their respective organizations, these were reported to be barriers at lower levels of the health system. Low perceived control of the RHIS data production process ultimately reduced RHIS data use for decision making among the strategic-level respondents. We recommend context-specific reexamination of existing RHIS interventions with a renewed emphasis on behavioral aspects of data use. The IBM can help guide practitioners, policy makers, and academics to address multiple socioecological factors that influence data use behavior when recommending RHIS and data use solutions.

Evaluation of coating uniformity for the digestion-aid tablets by portable near-infrared spectroscopy

Process analysis can effectively stabilize pharmaceutical quality and optimize the control of production process. For the sustained-release digestion-aid tablets, the coating film thickness is an important indicator to measure the quality of products. Traditional method mainly spot-checks tablets and measures with visual microscopy, which is time-consuming and laborious. This study attempted to use a portable near-infrared spectroscopy for rapid detection of a Chinese medicine tablets from production line. First, PLS regression models were established for coating film at twelve different locations of the tablet section, and the results showed that the correlation coefficients of training and validation sets were all over 0.80. Subsequently, the twelve locations were divided into six groups to further establish regressions. After chemometrics optimization, the optimal of six group models were generally better than single location models, with Rc2 and Rv2 all above 0.85, and RMSEV values all below 2.0. The mean relative error of prediction of the optimal model was 9.49%. The pharmaceutical process detection based on the portable NIR spectroscopy met the demand of managing digestion-aid tablet coating data conveniently. The proposed approach can successfully realize on-site and online pharmaceutical monitoring and has a promising practical value.

Dynamic Production Control and Optimization under Low Carbon Economy considering Delivery Time Demand.

Production control plays an important role in the efficient use of production resources. To promote energy-saving and environmental protection, low carbon emissions, and sustainable development of enterprises, it is necessary to explore the production control scheme that facilitates the manufacturing of low-cost products. However, the existing studies have not incorporated expected delivery time into the preparation of production control scheme. With the purpose of improving the low carbon economy, this paper integrates the quantitative data of the dynamic production control process to reduce the overall cost and solves the problems of collaborative operation between dynamic production control and delivery time demand, aiming to promote the green and sustainable development of production control technologies of the manufacturing industry. To solve the problems more scientifically, the following works were done: firstly, a dynamic production control model was established under the constraint of low carbon emissions. Next, the corporate manufacturing information was synthetized with the data on production, logistics, and delivery, and a collaborative optimization model was established for the dynamic production control and delivery time demand under low carbon economy. The effectiveness of our model was demonstrated through experiments.