Applying digital product memories in industrial production

Industrial production and supply chains face increased demands for mass customization and tightening regulations on the traceability of goods, leading to higher requirements concerning flexibility, adaptability, and transparency of processes. Technologies for the ’Internet of Things' such as smart products and semantic representations pave the way for future factories and supply chains to fulfill these challenging market demands. In this chapter a backend-independent approach for information exchange in open-loop production processes based on Digital Product Memories DPMs is presented. By storing order-related data directly on the item, relevant lifecycle information is attached to the product itself. In this way, information handover between several stages of the value chain with focus on the manufacturing phase of a product has been realized. In order to report best practices regarding the application of DPM in the domain of industrial production, system prototype implementations focusing on the use case of producing and handling a smart drug case are illustrated.

Towards the Industrial Production of Omega-3 Long Chain Polyunsaturated Fatty Acids from a Genetically Modified Diatom Phaeodactylum tricornutum.

The marine diatom Phaeodactylum tricornutum can accumulate up to 30% of the omega-3 long chain polyunsaturated fatty acid (LC-PUFA) eicosapentaenoic acid (EPA) and, as such, is considered a good source for the industrial production of EPA. However, P. tricornutum does not naturally accumulate significant levels of the more valuable omega-3 LC-PUFA docosahexaenoic acid (DHA). Previously, we have engineered P. tricornutum to accumulate elevated levels of DHA and docosapentaenoic acid (DPA) by overexpressing heterologous genes encoding enzyme activities of the LC-PUFA biosynthetic pathway. Here, the transgenic strain Pt_Elo5 has been investigated for the scalable production of EPA and DHA. Studies have been performed at the laboratory scale on the cultures growing in up to 1 L flasks a 3.5 L bubble column, a 550 L closed photobioreactor and a 1250 L raceway pond with artificial illumination. Detailed studies were carried out on the effect of different media, carbon sources and illumination on omega-3 LC-PUFAs production by transgenic strain Pt_Elo5 and wild type P. tricornutum grown in 3.5 L bubble columns. The highest content of DHA (7.5% of total fatty acids, TFA) in transgenic strain was achieved in cultures grown in seawater salts, Instant Ocean (IO), supplemented with F/2 nutrients (F2N) under continuous light. After identifying the optimal conditions for omega-3 LC-PUFA accumulation in the small-scale experiments we compared EPA and DHA levels of the transgenic strain grown in a larger fence-style tubular photobioreactor and a raceway pond. We observed a significant production of DHA over EPA, generating an EPA/DPA/DHA profile of 8.7%/4.5%/12.3% of TFA in cells grown in a photobioreactor, equivalent to 6.4 μg/mg dry weight DHA in a mid-exponentially growing algal culture. Omega-3 LC-PUFAs production in a raceway pond at ambient temperature but supplemented with artificial illumination (110 μmol photons m-2s-1) on a 16:8h light:dark cycle, in natural seawater and F/2 nutrients was 24.8% EPA and 10.3% DHA. Transgenic strain grown in RP produced the highest levels of EPA (12.8%) incorporated in neutral lipids. However, the highest partitioning of DHA in neutral lipids was observed in cultures grown in PBR (7.1%). Our results clearly demonstrate the potential for the development of the transgenic Pt_Elo5 as a platform for the commercial production of EPA and DHA.

Forecast comparison with nonlinear methods for brazilian industrial production

Este trabalho avalia as previsões de três métodos não lineares — Markov Switching Autoregressive Model, Logistic Smooth Transition Autoregressive Model e Autometrics com Dummy Saturation — para a produção industrial mensal brasileira e testa se elas são mais precisas que aquelas de preditores naive, como o modelo autorregressivo de ordem p e o mecanismo de double differencing. Os resultados mostram que a saturação com dummies de degrau e o Logistic Smooth Transition Autoregressive Model podem ser superiores ao mecanismo de double differencing, mas o modelo linear autoregressivo é mais preciso que todos os outros métodos analisados.

Forecast comparison with nonlinear methods for Brazilian industrial production

This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed.