Chapter A Framework for Learning System for Complex Industrial Processes

Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality and process efficiency while reducing the production cost and plant downtime are matters of utmost importance. These objectives are somewhat counteracting, and to satisfy them, optimal operation and control of the plant components are essential. Use of optimization not only improves the control and monitoring of assets, but also offers better coordination among different assets. Thus, it can lead to extensive savings in the energy and resource consumption, and consequently offer reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks. In this chapter, a generic learning system architecture is presented that can be retrofitted to existing automation platforms of different industrial plants. The architecture offers flexibility and modularity, so that relevant functionalities can be selected for a specific plant on an as-needed basis. Various functionalities such as soft-sensors, outputs prediction, model adaptation, control optimization, anomaly detection, diagnostics and decision supports are discussed in detail

Speed profile optimization of catenary-free electric trains with lithium-ion batteries

Catenary-free operated electric trains, as one of the recent technologies in railway transportation, has opened a new field of research: speed profile optimization and energy optimal operation of catenary-free operated electric trains. A well-formulated solution for this problem should consider the characteristics of the energy storage device using validated models and methods. This paper discusses the consideration of the lithium-ion battery behavior in the problem of speed profile optimization of catenary-free operated electric trains. We combine the single mass point train model with an electrical battery model and apply a dynamic programming approach to minimize the charge taken from the battery during the catenary-free operation. The models and the method are validated and evaluated against experimental data gathered from the test runs of an actual battery-driven train tested in Essex, UK. The results show a significant potential in energy saving. Moreover, we show that the optimum speed profiles generated using our approach consume less charge from the battery compared to the previous approaches. Document type: Articl

A Framework for Learning System for Complex Industrial Processes

Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality and process efficiency while reducing the production cost and plant downtime are matters of utmost importance. These objectives are somewhat counteracting, and to satisfy them, optimal operation and control of the plant components are essential. Use of optimization not only improves the control and monitoring of assets, but also offers better coordination among different assets. Thus, it can lead to extensive savings in the energy and resource consumption, and consequently offer reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks. In this chapter, a generic learning system architecture is presented that can be retrofitted to existing automation platforms of different industrial plants. The architecture offers flexibility and modularity, so that relevant functionalities can be selected for a specific plant on an as-needed basis. Various functionalities such as soft-sensors, outputs prediction, model adaptation, control optimization, anomaly detection, diagnostics and decision supports are discussed in detail

How to become independent of fossil fuels in Sweden

Sweden has got the toughest demand in the whole of Europe recently. In 2020 minimum 49 % of the energy should be renewable energy. To achieve the goal biogas production is being optimized, utilizing organic wastes and crops, to produce methane for cars and buses. In Vasteras a 200 MW waste gasification plant will be built to replace coal in an existing 600 MW PC-boiler with biogas. The plant will start up 2011. There will be co-firing with also peat, aside of the biogas. In Sweden 120 TWh/y of biomass is consumed, which is almost 1/3 of the total 400 TWh energy utilized annually. Most of it is used in co-generation (CHP) or pulp and paper industry. Now the plan is to increase production of liquid fuels for vehicles. Energy balances for production of bio ethanol in Sweden will be discussed. This can be an interesting part of poly-generation systems. Plug-in hybrid car are foreseen to be introduced on a large scale within the next 10 years. Here liquid fuels are used in a combustor with e.g. a turbine and generator primarily to produce electricity, while electric engines fed by electricity from batteries drive the vehicle. Today 60 % of the new cars are "environmental", that is low consuming diesel, ethanol or biogas. Seven years ago it was only 5 % of the new cars! Cities, county authorities and government are working together with companies and universities to drive the transfer away from fossil fuels

Biomass in different biotopes - an extensive resource

In this chapter, an overview will be made of the available biomass resources, primarily in Europe, covering different climate zones from north to south, from arctic over boreal into Mediterranean temperate or ubtropical dimates. Europe can be seen as an example of what is actually very similar around the globe in Russia, China and North America. Some other climates and biotopes are also covered as comparison especially the tropics in West Africa (Twenneboah, 2000) and sub-tropics in South Africa. The intention is to give more details than the chapter on the global resources, where the coverage was more general. Much of the data comes from Eurostat (2010) and World Development Lndicators (2011). Concerning specific data on selected crop sources are among other Salisbury and Ross (1992) and Weidow (1998)

Modelling of the channelling phenomenon of the pulping digester using porous Media

A dynamic model is developed for a continuous pulping digester to characterize the channeling phenomenon. The commercial CFD codes, FLUENT, are used to compute the hydraulic behavior of the digester under the normal and particular operational conditions. All the circulation flows, extractions, multiple inlets and outlets are included in this model. The digester is designed in the Gambit and an axisymmetric 2d model is applied. A porous scheme is implemented in the model in order to design the fluid flow and channelling phenomenon inside the digester. A heterogeneous porous media is specified in order to take the compression of the pulp into account. The simulation can serves as a prognosis model to predict the risk situations and probably to hinder high economical damage. The k- model contributes to compute the flow “regimes” or eventual eddies in some turbulent zones of the digester