Value stream mapping approach and analytical network process to identify and prioritize production system’s Mudas (case study: natural fibre clothing manufacturing company)

The current research has been done to identify and prioritize the Mudas of the production line of a natural fibre clothing manufacturing company; in this way, value stream mapping has been used to identify the Mudas, then analytical hierarchy method has been used to screen the Mudas. In the first step, necessary information to design value stream of production process has been obtained by observation, measurement and interview; afterward, according to the obtained information and by forming a focus group, Mudas in the production line have been identified and prioritized according to seven categories of Mudas. In order to prioritizing the Mudas by the usage of analytical hierarchy method, the Super Decision software has been applied. Then, according to the obtained priorities, suggested map of the future value system has been drawn and some suggestions have been presented to eliminate Mudas and move towards the lean manufacturing.     

Biodegradability Study of the Biosurfactant Contained in a Crude Extract from Corn Steep Water

Over the last few years, the global biosurfactant market has raised due to the increasing awareness among consumers, for the use of biological or bio-based products. Because of their composition, it can be speculated that these are more biocompatible and more biodegradable than their chemical homologous. However, at the moment, no studies exist in the literature about the biodegradability of biosurfactants. In this work, a biosurfactant contained in a crude extract, obtained from a corn wet-milling industry stream that ferments spontaneously in the presence of lactic acid bacteria, was subjected to a biodegradation study, without addition of external microbial biomass, under different conditions of temperature (5–45 °C), biodegradation time (15–55 days), and pH (5–7). For that, a Box–Behnken factorial design was applied, which allowed to predict the percentage of biodegradation for the biosurfactant contained in the crude extract, between the range of the independent variables selected in the study, obtaining biodegradation values between 3 and 80%. The percentage of biodegradation for the biosurfactant was calculated based on the increase in the surface tension of samples of the crude extract. Furthermore, it was also possible to predict the variation in t_1/2 for the biosurfactant (time to achieve the 50% of biodegradation) under different conditions.     

Adaptive neural network command filtered backstepping control of pure‐feedback systems in presence of full state constraints

An adaptive neural network (NN) command filtered backstepping control is proposed for the pure‐feedback system subjected to time‐varying output/stated constraints. By introducing a one‐to‐one nonlinear mapping, the obstacle caused by full stated constraints is conquered. The adaptive control law is constructed by command filtered backstepping technology and radial basis function NNs, where only one learning parameter needs to be updated online. The stability analysis via nonlinear small‐gain theorem shows that all the signals in closed‐loop system are semiglobal uniformly ultimately bounded. The simulation examples demonstrate the effectiveness of the proposed control scheme.     

An E-Business Event Stream Mechanism for Improving User Tracing Processes

With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record infinite user transactions. This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file. Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations. The experimental results are conducted by applying clustering-based classification algorithms on two different datasets; namely, Online Shoppers Purchasing Intention and Instacart Market Basket Analysis. The clustering process is used to group related objects into the same cluster, then the classification process measures the predicted classes of clustered objects. The experimental results record provable accuracy in predicting user preferences on both datasets.     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Preparative Protein Crystallization

Preparative protein crystallization can possibly replace one or more chromatographic steps in downstream processing. The development of such crystallization processes is demanding: first, promising principal crystallization conditions must be identified; second, details about the process must be defined; and third, the crystals have to be separated from the mother liquor without putting harm of their crystalline integrity. State‐of‐the‐art about these three steps is developing fast by (i) employing new screening methods which are based on fundamental understanding of the interaction of the protein molecules, (ii) application of existing concepts of technical bulk crystallization of small molecules to preparative protein crystallization, and (iii) making available specific gentle separation machinery.     

撞击流反应器撞击面稳定性研究进展

撞击流技术因其良好的混合特性近些年用于强化制备超细粉体反应中的混合过程。撞击面的稳定影响反应器内的混合效果，所以本文对撞击面稳定性的研究进行了综述。撞击流反应器不同结构形式包括平面撞击流、轴对称撞击流和微型撞击流等。文中简述了撞击流稳定性的实验研究手段，分析轴对称撞击流反应器的径向偏转振荡的起止条件和不同喷嘴间距下的轴向偏移振荡规律，并且分析平面撞击流反应器的撞击面偏转周期以及偏转振荡的起止条件。得出轴对称撞击流与平面撞击流撞击面驻点的振荡对混合都有促进作用，并且偏移振荡周期不定，轴对称撞击面偏移振幅与喷嘴间距和雷诺数相关。平面撞击流的偏转振荡周期与进口流速成反比，反应器结构参数是撞击流稳定性的影响因素之一。根据轴对称撞击流偏移振荡对混合的促进作用，本文提出一种新型的预设流量波形双组撞击流反应器。新型撞击流反应器的独特结构克服了物料反应通道单一缺点，通过预设波形控制其进口流量，增大其撞击面偏移振幅，消除撞击面无序振荡，使流动轨迹扩展，扩大混合区域，并设计实验装置与方法讨论动态流量撞击流反应器撞击面稳定性对混合效果的影响。最后，本文对轴对称撞击流反应器的混合性能研究前景进行展望。     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Malic acid production from renewables: a review

Malic acid derived from fossil resources is currently applied in the food and beverage industries with a medium global production capacity. However, in the transition from a fossil-based to a bio-based economy, biotechnologically produced l -malic acid may become an important platform chemical with many new applications, especially in the field of biopolymers. In this review, currently used petrochemical production routes to dl -malic acid are outlined and insights into possible bio-based alternatives for microbial l -malic acid production are provided. Besides ecological reasons, the possibility to produce enantiopure l -malic acid by microbial fermentation is the biggest advantage over chemical synthesis. State-of-the-art and open challenges concerning production host engineering, substrate choice and downstream processing are addressed. With regard to production hosts, a literature overview is given covering the leading natural production strains of Aspergillus, Ustilago and Aureobasidium, as well as Escherichia coli as the most important engineered recombinant host. The utilization of renewable substrates as an alternative to glucose is emphasized in particular as a key aspect for a competitive bio-based production. Out of the alternative substrates discussed in this review, the industrial side-streams crude glycerol and molasses seem to be most promising for large-scale l -malic acid production. © 2019 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

Lean production system design for fishing net manufacturing using lean principles and simulation optimization

Value stream mapping (VSM) is a useful tool for describing the manufacturing state, especially for distinguishing between those activities that add value and those that do not. It can help in eliminating non-value activities and reducing the work in process (WIP) and thereby increase the service level. This research follows the guidelines for designing future state VSM. These guidelines consist of five factors which can be changed simply, without any investment. These five factors are (1) production unit; (2) pacemaker process; (3) number of batches; (4) production sequence; and (5) supermarket size. The five factors are applied to a fishing net manufacturing system. Using experimental design and a simulation optimizing tool, the five factors are optimized. The results show that the future state maps can increase service level and reduce WIP by at least 29.41% and 33.92% respectively. For the present study, the lean principles are innovatively adopted in solving a fishing net manufacturing system which is not a well-addressed problem in literature. In light of the promising empirical results, the proposed methodologies are also readily applicable to similar industries.