Torque minimisation of the 2-DOF serial manipulators based on minimum energy consideration and optimum mass redistribution

This paper deals with the analytically tractable solution for input torques minimisation of two degrees of freedom serial manipulators based on minimum energy control and optimal redistribution of movable masses. The minimisation problem is carried out in two steps: at first, the optimal trajectory of the manipulator is defined as a function, which leads to the minimisation of energy consumption. Then, by introducing the obtained trajectory into dynamic equations, the torques are reduced by using the optimal redistribution of movable masses, which is carried out via an adaptive counterweight system. For this purpose, the torques due to the dynamic loads of the counterweights are presented as a function of the counterweight positions. The conditions for optimal dynamic balancing are formulated by minimisation of the root-mean-square value of the input torque including the dynamic loads of the unbalanced manipulator and counterweights. The suggested approach is illustrated by numerical simulations carried out using ADAMS software.     

Efficiency enhancement for indirect vector-controlled induction motor drive

In this paper, efficiency enhancement algorithms are developed and implemented on an indirect vector-controlled three-phase induction motor (IM) drive, and its performance under different operating conditions is analysed. The controllable electrical losses in the IM are minimised through the optimal control of direct axis (d-axis) stator current, and improvement in motor efficiency is achieved by weakening the rotor flux. The optimal d-axis stator current is also estimated using particle swarm optimisation (PSO) to validate the results obtained through analytical control method. The developed algorithms are tested under various operating conditions and the dynamic performance of the IM drive is analysed. The effectiveness of analytical and PSO-based efficiency optimisation control over conventional constant flux control, especially during light load at rated speed operation, is summarised. The effectiveness of the developed algorithm is validated experimentally through development of laboratory prototype set-up. The effect of parametric variation on efficiency, stator current, torque and speed of IM drive is studied through sensitivity analysis. The effect of variation in stator and rotor resistance due to change in operating temperature of the IM is also analysed and the robustness of the developed algorithm against parametric variations is demonstrated through simulation and experimental studies.     

Biocatalytic electrode improvement strategies in microbial fuel cell systems

Microbial fuel cells (MFCs) produce electricity as a result of the microbial metabolism of organic substrates, hence they represent a sustainable approach for energy production and waste treatment. If the technology is to be implemented in industry, low cost and sustainable bioelectrodes must be developed to increase power output, increase waste treatment capacity, and improve service intervals. Although the current application of abiotic electrode catalysts, such as platinum and electrode binders such as Nafion leads to greater MFC performance, their use is cost prohibitive. Novel bioelectrodes which use cost effective and sustainable materials are being developed. These electrodes are developed with the intention to reduce start-up time, reduce costs, extend life-span and improve core MFC performance metrics (i.e. power density, current density, chemical oxygen demand (COD) reduction and Coulombic efficiency (CE)). Comparison of different MFC systems is not an easy task. This is due to variations in MFC design, construction, operation, and different inocula (in the case of mixed-culture MFCs). This high intra-system variability should be considered when assessing MFC data, operation and performance. This review article examines the major issues surrounding bioanode and biocathode improvement in different MFC systems, with the ultimate goal of streamlining and standardising improvement processes. © 2018 Society of Chemical Industry     

A highway in state space for reactors with minimum entropy production

Thousands of numerical solutions of an optimal control problem for plug flow reactors were found to give, what we call a “highway in the reactors’ state space”. The problem was to find the heat transfer strategy which minimise the entropy production in reactors with fixed chemical conversion. The control variable was always the temperature of the heating/cooling medium along the reactor. The highway represents the most energy efficient way to travel far in state space. Such highways were studied for five reactor systems, endothermic and exothermic ones. Numerical analysis showed that the reactor highway is characterised by approximately constant thermodynamic driving forces/local entropy production for reasonable process intensities. Each solution represents a compromise between the entropy production of reactions, heat transfer and frictional flow (pressure drop). The solutions enter and leave the highway at different positions depending on how far from the highway their initial and final destinations are. Knowledge about the nature of the highway, e.g. when the reactor operates in a reaction mode or a heat transfer mode, may be important for energy efficient reactor design. The theoretical formulation of the optimisation problem is valid for plug flow as well as batch reactors. We showed that important results in literature like the Spirkl-Ries quantity, the theorems of equipartition of entropy production and equipartition of forces are contained in our general formulation. The numerical results showed that the analytical results are good approximations to the optimum also in problems where they do not apply in a strictly mathematical sense.     

Identification of polynomial input/output recursive models with simulation error minimisation methods

Polynomial input/output (I/O) recursive models are widely used in nonlinear model identification for their flexibility and representation capabilities. Several identification algorithms are available in the literature, which deal with both model selection and parameter estimation. Previous works have shown the limitations of the classical prediction error minimisation approach in this context, especially (but not only) when the disturbance contribution is unknown, and suggested the use of a simulation error minimisation (SEM) approach for a better selection of the I/O model. This article goes a step further by integrating the model selection procedure with a simulation-oriented parameter estimation algorithm. Notwithstanding the algorithmic and computational complexity of the proposed method, it is shown that it can sometimes achieve great performance improvements with respect to previously proposed approaches. Two different parameter estimation algorithms are suggested, namely a direct SEM optimisation algorithm, and an approximate method based on multi-step prediction iteration, which displays several convenient properties from the computational point of view. Several simulation examples are shown to demonstrate the effectiveness of the suggested SEM approaches.     

A mean field annealing approach to accurate free form shape matching

The SoftAssign algorithm is an elegant free form shape matching algorithm. While its objective function can be interpreted as consisting of three desired terms: minimising a weighted sum of matching errors of combinations of all the points in the two free form shapes to be matched, equalising their weights (probabilities) of being real ones and also maximising the overlapping area between the free form shapes to be matched, the last term has no effect on the optimisation of the parameters of interest due to normalisation. In this paper, we reformulate the last two terms using the inequality about the geometric and algebraic averages and the sum of the powers of these probabilities. For the sake of computational efficiency, instead of considering combinations of all the points in the overlapping free form shapes to be matched, we employ the traditional closest point criterion to establish possible correspondences between the two overlapping free form shapes to be matched. The saddle point solution of the resulting objective function no longer yields a closed form solution to the parameters of interest. For easy computation, we then adopt a pseudo-linearisation method to linearise the first order derivative of the objective function, leading the parameters of interest to be tracked and estimated with a closed form solution. The parameters of interest are finally optimised using the efficient deterministic annealing scheme with the camera motion parameters estimated using the quaternion method in the weighted least squares sense. A comparative study based on both synthetic data and real images with partial overlap has shown that the proposed algorithm is promising for the automatic matching of overlapping 3D free form shapes subject to a large range of motions.     

基于几何流形能量最小化的图像检索算法

提出了一种基于几何流形能量GEOMEN(geometric manifold energy)最小化的图像检索算法。许多基于流形的检索算法都是在图像的特征空间提取相应的语义流形空间,进而在语义空间中进行图像检索排序。将图像的检索看作一个图像数据库中搜索一个最优图像能量环的问题。图像能量环表示了图像之间的联系和相关性,通过最小化GEOMEN可以得到最优图像环。最小化的求解涉及到一个组合优化的问题,传统的禁忌搜索算法在选择最优候选集时非常耗时,提出一种智能的积极禁忌搜索算法求解最优环,实验表明提出的算法检索性能高,可以得到较高的查全率与准确率。     

An iterative algorithm for simulation error based identification of polynomial input–output models using multi-step prediction

Effective identification of polynomial input–output models for applications requiring long-range prediction or simulation performance relies on both careful model selection and accurate parameter estimation. The simulation error minimisation (SEM) approach has been shown to provide significant advantages in the model selection phase by ruling out candidate models with good short-term prediction capabilities but unsuitable long-term dynamics. However, SEM-based parameter estimation has been generally avoided due to excessive computational effort. This article extends to the nonlinear case a computationally efficient approach for this task, that was previously developed for linear models, based on the iterative estimation of predictors with increasing prediction horizon. Conditions for the applicability of the approach to various model classes are also discussed. Finally, some examples are provided to show the effectiveness and computational convenience of the proposed algorithm for polynomial input–output identification, as well as the improvements achievable by enforcing SEM parameter estimation. A benchmark for nonlinear identification is also analysed, with encouraging results.     

Analysis and integration of fuel cell combined cycles for development of low-carbon energy technologies

Integrated and combined cycles (ICC, CC) traditionally involve gas and steam turbines only. The paper analyses the further integration of high-temperature fuel cells (FC) having high electrical efficiency reaching up to 60% compared with 30–35% for most gas turbines. The previous research on FC hybrids indicates achieving high efficiencies and economic viability is possible. The ICC of various FC types—their performance and the potential for utilisation of renewables—are analysed considering also power generation capacity and site heat integration context. Further research and development with industrial relevance are outlined focusing on CO₂ emissions reduction.