Robust, fast and accurate pH sensing platform solution targeting Single Use application

QbD in biotech and life-sciences necessitates good process metrics in drug discovery, development and manufacturing. Accurate knowledge of pH measurements remains fundamentally important. There is much to be desired with use of conventional glass potentiometric probes for monitoring pH. Challenges being measurement accuracy, system drift, and need for frequent calibration. Additionally glass being fragile compounds the situation further in manufacturing. Glass probes for pH measurement are really unattractive for single use system applications (SUS). Optical based solutions while easy to integrate lack range, need calibration and are a problem in storage (application timing). We demonstrate a robust Platform Solution tested for a variety of applications that includes environs demanding repeated use and SUS. The innovative solution deploys electrochemical based redox chemistry engineered to deliver a scalable (semiconductor technologies) non-glass, calibration-free, drift-free, robust sensing platform. The System Solution is flexible and simply packaged as a 2D ‘sticker’ easily molded into a bag or a 3D probe for insertion in typical 12mm ports in-place for glass probes. Technical performance data will be presented indicating wide performance range (pH 3.0 – 12.0), accuracy (+/- 0.1), covering 5o C – 50o C, calibration-free (factory set, and stable), drift-free (over process duration greater than 35 days), ability to withstand Clean-In-Place (CIP), Steam-In-Place, and gamma irradiation expected of SUS as well as up-stream and down-stream applications. The author will share experience driving six sigma methodologies in successful development of a pH sensing platform

Cloud-based manufacturing-as-a-service environment for customized products

This paper describes the paradigm of cloud-based services which are used to envisage a new generation of configurable manufacturing systems. Unlike previous approaches to mass customization (that simply reprogram individual machines to produce specific shapes) the system reported here is intended to enable the customized production of technologically complex products by dynamically configuring a manufacturing supply chain. In order to realize such a system, the resources (i.e. production capabilities) have to be designed to support collaboration throughout the whole production network, including their adaption to customer-specific production. The flexible service composition as well as the appropriate IT services required for its realization show many analogies with common cloud computing approaches. For this reason, this paper describes the motivation and challenges that are related to cloud-based manufacturing and illustrates emerging technologies supporting this vision byestablishing an appropriate Manufacturing-as-a-Service environment based on manufacturing service descriptions

Incorporating the knowledge management cycle in e-business

In e-business, knowledge can be extracted from the recorded information by intelligent data analysis and then utilised in the business transaction. E-knowledge is a foundation for e-business. E-business can be supported by an intelligent information system that provides intelligent business process support and advanced support of the e-knowledge management cycle. Knowledge is stored as knowledge models that can be updated in the e-knowledge management cycle. As illustrated in examples, the e-knowledge cycle aids in the business decision taking, production management, and costs management

Semantic web service architecture for simulation model reuse

COTS simulation packages (CSPs) have proved popular in an industrial setting with a number of software vendors. In contrast, options for re-using existing models seem more limited. Re-use of simulation component models by collaborating organizations is restricted by the same semantic issues however that restrict the inter-organization use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architecture provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontology to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development of ontology, connector software and web service discovery architecture in order to understand how such ontology are created, maintained and subsequently used for simulation model reuse. The ontology is extracted from health service simulation - comprising hospitals and the National Blood Service. The ontology engineering framework and discovery architecture provide a novel approach to inter- organization simulation, uncovering domain semantics and adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community

A Web Services Component Discovery and Deployment Architecture for Simulation Model Reuse

CSPs are widely used in industry, although have yet to operate across organizational boundaries. Reuse across organizations is restricted by the same semantic issues that restrict the inter-organization use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architecture provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontology to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development carried out within CSPI-PDG and Fluidity Group at Brunel University, of an ontology, connector software and web service discovery architecture. The ontology is extracted from simulation scenarios involving airport, restaurant and kitchen service suppliers. The ontology engineering framework and discovery architecture provide a novel approach to inter-organization simulation, adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community

A Layered Software Architecture for the Management of a Manufacturing Company

In this paper we describe a layered software architecture in the management of a manufactur-ing company that intensively uses computer technology. Application tools, new and legacy, after the updating, operate in a context of an open web oriented architecture. The software architecture enables the integration and interoperability among all tools that support business processes. Manufacturing Executive System and Text Mining tools are excellent interfaces, the former both for internal production and management processes and the latter for external processes coming from the market. In this way, it is possible to implement, a computer integrated factory, flexible and agile, that immediately responds to customer requirements.ICT, Service Oriented Architecture, Web Services, Computer-Integrated Factory, Application Software

Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities