Simulation and optimisation techniques for sawmill yard operation : A literature review

Increasing costs and competitive business strategies are pushing sawmill enterprises to make an effort for optimization of their process management. Organizational decisions mainly concentrate on performance and reduction of operational costs in order to maintain profit margins. Although many efforts have been made, effective utilization of resources, optimal planning and maximum productivity in sawmill are still challenging to sawmill industries. Many researchers proposed the simulation models in combination with optimization techniques to address problems of integrated logistics optimization. The combination of simulation and optimization technique identifies the optimal strategy by simulating all complex behaviours of the system under consideration including objectives and constraints. During the past decade, an enormous number of studies were conducted to simulate operational inefficiencies in order to find optimal solutions. This paper gives a review on recent developments and challenges associated with simulation and optimization techniques. It was believed that the review would provide a perfect ground to the authors in pursuing further work in optimizing sawmill yard operations.

Evaluation of a thermally driven heat pump for solar heating and cooling applications

Exploiting solar energy technology for both heating and cooling purposes has the potential of meeting an appreciable portion of the energy demand in buildings throughout the year. By developing an integrated, multi-purpose solar energy system, that can operate all twelve months of the year, a high utilisation factor can be achieved which translates to more economical systems. However, there are still some techno-economic barriers to the general commercialisation and market penetration of such technologies. These are associated with high system and installation costs, significant system complexity, and lack of knowledge of system implementation and expected performance. A sorption heat pump module that can be integrated directly into a solar thermal collector has thus been developed in order to tackle the aforementioned market barriers. This has been designed for the development of cost-effective pre-engineered solar energy system kits that can provide both heating and cooling. This thesis summarises the characterisation studies of the operation of individual sorption modules, sorption module integrated solar collectors and a full solar heating and cooling system employing sorption module integrated collectors. Key performance indicators for the individual sorption modules showed cooling delivery for 6 hours at an average power of 40 W and a temperature lift of 21°C. Upon integration of the sorption modules into a solar collector, measured solar radiation energy to cooling energy conversion efficiencies (solar cooling COP) were between 0.10 and 0.25 with average cooling powers between 90 and 200 W/m2 collector aperture area. Further investigations of the sorption module integrated collectors implementation in a full solar heating and cooling system yielded electrical cooling COP ranging from 1.7 to 12.6 with an average of 10.6 for the test period. Additionally, simulations were performed to determine system energy and cost saving potential for various system sizes over a full year of operation for a 140 m2 single-family dwelling located in Madrid, Spain. Simulations yielded an annual solar fraction of 42% and potential cost savings of €386 per annum for a solar heating and cooling installation employing 20m2 of sorption integrated collectors.

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.