Zero carbon manufacturing facility - Towards integrating material, energy, and waste process flows

The increasing pressure on material availability, energy prices, as well as emerging environmental legislation is leading manufacturers to adopt solutions to reduce their material and energy consumption as well as their carbon footprint, thereby becoming more sustainable. Ultimately manufacturers could potentially become zero carbon by having zero net energy demand and zero waste across the supply chain. The literature on zero carbon manufacturing and the technologies that underpin it are growing, but there is little available on how a manufacturer undertakes the transition. Additionally, the work in this area is fragmented and clustered around technologies rather than around processes that link the technologies together. There is a need to better understand material, energy, and waste process flows in a manufacturing facility from a holistic viewpoint. With knowledge of the potential flows, design methodologies can be developed to enable zero carbon manufacturing facility creation. This paper explores the challenges faced when attempting to design a zero carbon manufacturing facility. A broad scope is adopted from legislation to technology and from low waste to consuming waste. A generic material, energy, and waste flow model is developed and presented to show the material, energy, and waste inputs and outputs for the manufacturing system and the supporting facility and, importantly, how they can potentially interact. Finally the application of the flow model in industrial applications is demonstrated to select appropriate technologies and configure them in an integrated way. © 2009 IMechE.

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward. This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters. © 2011 Elsevier Ltd. All rights reserved.

Design methodology for composite structures: A small low air-speed wind turbine blade case study

A small low air-speed wind turbine blade case study is used to demonstrate the effectiveness of a materials and design selection methodology described by Monroy Aceves et al. (2008) [24] for composite structures. The blade structure comprises a shell of uniform thickness and a unidirectional reinforcement. The shell outer geometry is fixed by aerodynamic considerations. A wide range of lay-ups are considered for the shell and reinforcement. Structural analysis is undertaken using the finite element method. Results are incorporated into a database for analysis using material selection software. A graphical selection stage is used to identify the lightest blade meeting appropriate design constraints. The proposed solution satisfies the design requirements and improves on the prototype benchmark by reducing the mass by almost 50%. The flexibility of the selection software in allowing identification of trends in the results and modifications to the selection criteria is demonstrated. Introducing a safety factor of two on the material failure stresses increases the mass by only 11%. The case study demonstrates that the proposed design methodology is useful in preliminary design where a very wide range of cases should be considered using relatively simple analysis. © 2011 Elsevier Ltd.

Frictional flow of damp granular material in a conical centrifuge

An analysis is given of velocity and pressure-dependent sliding flow of a thin layer of damp granular material in a spinning cone. Integral momentum equations for steady state, axisymmetric flow are derived using a boundary layer approximation. These reduce to two coupled first-order differential equations for the radial and circumferential sliding velocities. The influence of viscosity and friction coefficients and inlet boundary conditions is explored by presentation of a range of numerical results. In the absence of any interfacial shear traction the flow would, with increasing radial and circumferential slip, follow a trajectory from inlet according to conservation of angular momentum and kinetic energy. Increasing viscosity or friction reduces circumferential slip and, in general, increases the residence time of a particle in the cone. The residence time is practically insensitive to the inlet velocity. However, if the cone angle is very close to the friction angle then the residence time is extremely sensitive to the relative magnitude of these angles. © 2011 Authors.