Renewable Resources Management: Two Proposed Dynamic Optimal Control Models

This research regards to a two-dimensional lateral pushing nanomanipulation using Atomic Force Microscope (AFM). Yet a reliable control of the AFM tip position during the AFM-based manipulation process is a chief issue since the tip can jump over the target nanoparticle and then the process can fail. However, a detailed Modeling and understanding of the interaction forces on the AFM tip is important for prosperous manipulation control and a nanometer resolution tip positioning. In the proposed model, Lund-Grenoble (LuGre) dynamic friction model is used as friction force on the contact surface between the nanoparticle and the substrate. This model leads to a stick-slip behavior of the nanoparticle that is so similar to the experimental behavior in nanoscale. Derjaguin interaction force is applied between the AFM tip and the nanoparticle which considers both attractive and repulsive interactions. The AFM is modeled by lumped-parameters model. A controller is designed based on the proposed dynamic model in order for positioning the AFM tip during a desired nanomanipulation task. Optimal sliding mode approach is used to design the controller. In this approach sliding surface which is used in the sliding mode approach is selected optimally based on the linear quadratic (LQ) method.

This paper addresses the development of advanced computer control strategies and those applications in the thermal processes of metal industry. The process modelling involves the development of dynamic mathematic models with state space forms describing the thermal state in the heated and/or cooled objects. Dynamic model based optimal control strategies for both batch and continuous thermal processes in terms of the maximum principle, dynamic programming, heuristic search, etc. are also proposed in this study. The target of the development of optimal control for the heating processes is to provide the optimal heating patterns based on the given criteria and constraints associated with dynamic models and others. A complete hierarchical computer control structure for heating processes is also proposed. Finally, the application results of the proposed modern control strategies to a production scale slab reheating furnace are given.

Recently, neural network dynamic modeling has drawn a great deal of attention not only from academia but also from industry. It has been shown that neural network can learn to mimic the behavior of a complex chemical process. However, a neural network dynamic model might be difficult to develop when training data do not contain sufficient nonlinear information about the process. In addition, owing to the capacity of a modern control system, a great deal of steady-state data is stored. Most modeling techniques to date do not take into account the fact that the transient data may not contain sufficient nonlinear information and that the nonlinear steady-state data are abundant and easily accessible. In order to fully utilize the abundant steady-state information, we present a neural network Hammerstein (NNH) modeling approach. In the proposed approach, a static neural network is integrated with a linear dynamic model in series. A constrained gradient-based algorithm is derived for training the linear dynamic model in order to retain the nonlinearity established by the static neural network. To demonstrate the proposed modeling approach, a complex polymerization process is used as an example

Spatial economic modelling for renewable natural resource management

This article deals with a numerical analysis of the static and dynamic performance of a compliant journal gas bearing. The common approach found in foil bearing literature consists in calculating the carrying capacity for a given shaft position. In this study the external load is fixed (magnitude and direction) and the related shaft position is investigated. Nevertheless, a rigid profile, able to support high imposed loads, is no longer valid if one considers that the bearing becomes compliant. An original calculation method of the initial profile considering rigid surfaces is proposed to overcome this problem. The prediction of nonlinear dynamic behavior, i.e., stability and response to external excitation, is investigated. Finally, a viscous damping model is introduced into the dynamic model in order to obtain the amount of structural damping necessary to increase the stability of the compliant journal gas bearing.

Ventilation of Black Sea pycnocline by the Mediterranean plume

This paper presents an integrated Geosciences and Reservoir work approach used for complex fractured and karstified carbonate Kharyaga field. Synthesis is fulfilled for all available data, such as seismic interpretation, static well data, well tests etc. It has been launched on Kharyaga field mid 2012. Kharyaga field is a NW-SE elongated anticline lying 60km north of the Artic polar Circle, in Nenets Autonomous Territory on Kolva regional swell. Total E&P Russie (TEPR) is operating Object 2 and 3 of this field. The Object 2 accumulation, subject of this study, is characterised by oil bearing Devonian carbonate reservoir with a gross thickness of 160–250m with high vertical and lateral heteregenity. Current production from Object 2 is around 23 kbopd. The purpose of the synthesis is to create new and more reliable geological and dynamic models in order to better optimize next development phase and fine tune production profiles estimation. Geosciences and Reservoir synthesis imply the following: gathering all available information starting from newly reprocessed seismic data, various static well data and ending by well tests and production history over the whole life of the field,depth analysis and putting in coherence of all information in order to better address the various heterogeneity scales and reservoir characterization. There are two main challenges related to characterization of existing heterogeneities: defining the fault pattern including sub seismic faults, understanding karst and fractures distribution,defining vertical and lateral sedimentary heterogeneity as well as reservoir extension. Many experts and specialists are involved in this synthesis. The joint approach between geologist and geophysicist, based on tectno-stratigraphic timing and stress field combined with seismic attributes and thickness map, allowed to proposed a new fault pattern which honors FMI data and dynamic interference tests recorded during the 14 years of field production. Interpretation of newly reprocessed seismic data helped the sedimentologists in inter-well correlation. Looking just at seismic amplitudes cubes, it was possible to propose a new sedimentary model and change inter-well correlation accordingly. This led to the updated vision of the reservoir.

On the Choice of the Optimal Temporal Control in Renewable Resource Management

In order to imitate the dynamic decision and action mode of intelligent creatures responding to environmental changes, this paper presents a dynamic decision and control model based on neighborhood systems. Whose main idea is to break down a decision-making process which is dynamic and complex from a macro point of view, into a series of static decision-making processes which are simple from a microscopic point of view. Firstly, we suggest some methods to integrate neighborhood systems located in different metric spaces into one neighborhood system, and present two general models to describe the neighborhood systems and the differential neighborhood systems in normed linear spaces, respectively. Secondly, we propose a general programming model to select the optimal feasible neighborhood and satisfactory feasible neighborhood within neighborhood systems. The optimal or satisfactory neighborhood is just a simple region to make a decision or control action. And present a condition that the overall optimal control can be decomposed into the local optimal control. Finally, a decision-making process based neighborhood systems is shown in steps. The effectiveness of the presented models and methods are illustrated with an intelligent driving system.

Owing to the significance of urban water resources demand management, and in accordance with urban water demand with features of both growth and periodicity, an urban water resources supply and demand management dynamic optimal control model was constructed. On the basis of the prediction of urban water demand, the urban water demand could be satisfied through reasonable control of urban water supply investment. Under state space constraint, a complementary slackness stronger form, required by the method of applying maximum principle to solving optimal control problem, was analyzed not always to be true on the conjoint point where optimal trajectory changes from active into inactive. Then, the normal form of complementary slackness condition should be applied to solving the problem under the circumstances. By implemented singular optimal control on the system, the total square of deviation of urban water resources supply and demand could be minimized in the period of urban water demand growth.

A dynamic dehumidifier model for simulations and control of liquid desiccant hybrid air conditioning systems

This paper presents a review of recent developments that have taken place in the area of dynamic optimal control models in advertising subsequent to the comprehensive survey of the literature by Sethi in 1977. The basic problem underlying these models is that of determining optimal advertising expenditures and possibly other variables of interest over time for a firm or a group of competing firms under consideration. This optimization is done subject to some dynamics that define how these variables translate into sales and in turn, into profits. The purpose of this update is twofold. On the one hand, new contributions in the areas already treated in the earlier survey are reviewed. On the other hand, new trends in the advertising literature since 1977, such as quality as an additional marketing instrument, cumulative sales models, pulsing advertising, and advertising as a part of corporate models of the firm, are discussed. The models covered in this update are organized under six headings: models with capital stocks generated by advertising, price, and quality, sales-advertising response models, cumulative sales models for durable goods, models with more than one state variables in the advertising process, models incorporating interaction with other functional areas, and competitive models. The discussion involves specifications, methods used, results, empirical validation, if any, and their economic significance. The survey concludes with suggestions for extensions and future directions of research.

Many governments who preside over liberalised energy markets are developing policies aimed at promoting investment in renewable generation whilst maintaining the level of security of supply customers have come to expect. Of particular interest is the mix and amount of generation investment over time in response to policies promoting high penetrations of variable output renewable power such as wind. Modelling the dynamics of merchant generation investment in market environments can inform the debate. Such models need improved methods to calculate expected output, costs and revenue of thermal generation subject to varying load and random independent thermal outages in a power system with high penetrations of wind. This paper presents a dynamic simulation model of the aggregated Great Britain (GB) generation investment market. The short-term energy market is simulated using probabilistic production costing based on the Mix of Normals distribution technique with a residual load calculation (load net of wind output). Price mark-ups due to market power are accounted for. These models are embedded in a dynamic model in which generation companies use a Value at Risk (VaR) criterion for investment decisions. An `energy-only' market setting is used to estimate the economic profitability of investments and forecast the evolution of security of supply. Simulated results for the GB market case study show a pattern of increased relative security of supply risk during the 2020s. In addition, fixed cost recovery for many new investments can only occur during years in which more frequent supply shortages push energy prices higher. A sensitivity analyses on a number of key model assumptions provides insight into factors affecting the simulated timing and level of generation investment. This is achieved by considering the relative change in simulated levels of security of supply risk metric such as de-rated capacity margins and expected energy unserved. The model can be used as a decision support tool in policy design, in particular how to address the increased `energy-only market revenue risk facing thermal generation, particularly peaking units, that rely on a small number of high price periods to recover fixed costs and make adequate returns on investment.

Renewable resource management with a backstop substitute and nonautonomous prices

Hierarchical control model of AGC is adopted by regional power grid of China, which is divided into two levels, namely, one regional dispatching center (RD) and several provincial dispatching centers (PD). The RD and each PD have their own control assessment objectives, however, the load, RD and PD AGC units are located in the different PD control areas. Hence the cross-regulation uncoordinated control of RD and PD AGC units is not only affects the control performance standard (CPS) of the PD but also directly impacts the adjustment cost of AGC units of them. Aiming at this problem, a dynamic coordination optimization model of regional and provincial AGC unit control for the interconnected power system (DCOMAGCUC) is proposed in this paper to improve the effect and adjustment performance of AGC strategy. In this control strategy, the minimum AGC ancillary service cost is considered as objective function, the power balances function of the tie line between different province power girds and the ultra-high voltage (UHV) line are considered as equality constraints, the CPS1 and CPS2 of each PD control area, the UHV line between the different regional power grids and other unit's constraints are considered as inequality constraints, and the adjustment instruction and the adjustment rate of all AGC units of RD and PD are considered as control variables, and the tie line power deviation as well as frequency deviation as dependent variables. The model of DCOMAGCUC is a complex nonlinear optimization problem with continuous and discrete variables. Then, an immunity evolutionary programming algorithm is adopted to solve the DCOMAGCUC. The experiment results and comparisons are obtained by DCOMAGCUC and the traditional PI control strategy, which indicated that the proposed DCOMAGCUC can enhance the effect and performance of AGC