Evolutionary design of dynamic neural networks for evaporator control

This paper presents a novel neural network (NN) to control an ammonia refrigerant evaporator. Inspired by the latest findings on the biological neuron, a dynamic synaptic unit (DSU) is proposed to enhance the information processing capacity of artificial neurons. Treating the dynamic synaptic activity after the nonlinear somatic activity helps to capture the dynamics demarcated by the Gaussian activation pertaining to the input space. This practice leads to a remarkable reduction in curse of dimensionality. The proposed NN architecture has been compared with two other conventional architectures; one with dynamic neural units (DNUs) and the other with nonlinear static functions as perceptrons. The objective is to control evaporator heat flow rate and secondary fluid outlet temperature while keeping the degree of refrigerant superheat in the range 4–7 K at the evaporator outlet by manipulating refrigerant and evaporator secondary fluid flow rates. The drawbacks of conventional approaches to this problem are discussed, and how the novel method can overcome them are presented. An evolutionary approach is adopted to optimize the parameters of the NN controllers. Then evaporator process model is accomplished as a combination of governing equations and a sub NN resulting in a simple and sufficiently accurate model. The effectiveness of the proposed dynamic NN controller for the evaporator system model is validated using experimental data from the ammonia refrigeration plant.     

Wiener models of direction-dependent dynamic systems

Analysis of systems with direction-dependent dynamics is currently limited to cases in which the dynamics in the two directions of the output are first order; results for such systems have been published for both pseudo-random maximum-length binary (MLB) and inverse-repeat maximum-length binary (IR-MLB) inputs. These relatively limited analytical results make it useful to examine alternative ways of modelling such systems and in this paper, Wiener models are considered for this purpose. Methods for optimising the Wiener model parameters by matching the system and model cross-correlation functions, outputs, and discrete Fourier transforms of the outputs are considered, and the results are compared. These methods are also applied to a first-order direction-dependent system with a maximum-length ternary (MLT) input, for which no analytical results are currently available, and to a second-order system with an IR-MLB input.     

Process optimisation in sunflower oil extraction by supercritical CO2

A continuous process for the extraction of sunflower oil using supercritical CO₂, featuring multiple extractors, one oil separator and three cascaded CO₂ recovery vessels operating at different pressures, was devised and studied. For every single equipment of the plant making up the process a mathematical model was built. Experimental tests—consisting in measurements of oil solubility in supercritical CO₂—were carried out in a laboratory-scale apparatus to characterise the behaviour of sunflower oil in the separation from the supercritical fluid. The mathematical model of the whole process was coded in the commercial gPROMS process modelling environment where both its simulation and optimisation—this latter assuming the overall oil production cost as the objective function—were carried out. The process- and economics-related results are discussed and compared with those obtained with traditional and cold-pressing extraction.     

Optimal control and operation of drum granulation processes

Process optimisation and optimal control of batch and continuous drum granulation processes are studied in this paper. The main focus of the current research has been: (i) construction of optimisation and control relevant, population balance models through the incorporation of moisture content, drum rotation rate and bed depth into the coalescence kernels; (ii) investigation of optimal operational conditions using constrained optimisation techniques; (iii) development of optimal control algorithms based on discretized population balance equations; and (iv) comprehensive simulation studies on optimal control of both batch and continuous granulation processes. The objective of steady state optimisation is to minimise the recycle rate with minimum cost for continuous processes. It has been identified that the drum rotation-rate, bed depth (material charge), and moisture content of solids are practical decision (design) parameters for system optimisation. The objective for the optimal control of batch granulation processes is to maximize the mass of product-sized particles with minimum time and binder consumption. The objective for the optimal control of the continuous process is to drive the process from one steady state to another in a minimum time with minimum binder consumption, which is also known as the state-driving problem. It has been known for some time that the binder spray-rate is the most effective control (manipulative) variable. Although other possible manipulative variables, such as feed flow-rate and additional powder flow-rate have been investigated in the complete research project, only the single input problem with the binder spray rate as the manipulative variable is addressed in the paper to demonstrate the methodology. It can be shown from simulation results that the proposed models are suitable for control and optimisation studies, and the optimisation algorithms connected with either steady state or dynamic models are successful for the determination of optimal operational conditions and dynamic trajectories with good convergence properties.     

Reliability evaluation and optimisation of imperfect inspections for a component with multi-defects

In this paper, a model is developed to evaluate the reliability and optimise the inspection schedule for a multi-defect component. The model uses a non-homogeneous Poisson process (NHPP) method in conjunction with a delay time approach. The inspections are designed to detect any defects in the component, however it can be imperfect. The defect is a definable state before a functional failure happens to the component. Occurrences of defects are assumed to follow an NHPP and a defect will be minimally repaired if it is identified during an inspection. It is shown that the failures occurring in an interval of inspection will also follow an NHPP. The situation of imperfect inspections and non-constant inspection intervals are considered. An algorithm is presented to optimise the intervals of inspections in order to maximise the reliability of the component, and the properties of the algorithm are shown. A numerical example with parametric study is given to show the performance of the model and the algorithm.     

Structural and surface properties of thermally prepared Ti/RhxTi(1−x)Oy electrodes: I—influence of preparation parameters

Electrodes of Rh_xTi_(1−x)O_y nominal composition were prepared by thermal decomposition of the chloride or nitrate precursor salts dissolved in strongly acidic medium and applied by brush to both sides of a Ti° support. A systematic study of the influence of calcination temperature and time as well as oxygen flux was conducted. The coatings were characterised by SEM, EDAX, XRD, open circuit potential measurements and cyclic voltammetry (CV). Visible-ultraviolet spectrophotometry was employed to identify the chemical form of the precursor in solution while thermogravimetric analysis (TGA) was used to assess the decomposition temperature ranges. Optimisation of the coating preparation parameters showed coatings obtained from [Rh(H₂O)₆](NO₃)₃ precursor dissolved in HNO₃ 1:2 (v/v) and fired at 430 °C for 2 h in a 5 l min⁻¹ oxygen stream-furnished stable electrodes having the highest electrochemically active surface area.     

Rescheduling policies for large-scale task allocation of autonomous straddle carriers under uncertainty at automated container terminals

This paper investigates replanning strategies for container-transportation task allocation of autonomous Straddle Carriers (SC) at automated container terminals. The strategies address the problem of large-scale scheduling in the context of uncertainty (especially uncertainty associated with unexpected events such as the arrival of a new task). Two rescheduling policies–Rescheduling New arrival Jobs (RNJ) policy and Rescheduling Combination of new and unexecuted Jobs (RCJ) policy–are presented and compared for long-term Autonomous SC Scheduling (ASCS) under the uncertainty of new job arrival. The long-term performance of the two rescheduling policies is evaluated using a multi-objective cost function (i.e., the sum of the costs of SC travelling, SC waiting, and delay of finishing high-priority jobs). This evaluation is conducted based on two different ASCS solving algorithms–an exact algorithm (i.e., branch-and-bound with column generation (BBCG) algorithm) and an approximate algorithm (i.e., auction algorithm)–to get the schedule of each short-term planning for the policy. Based on the map of an actual fully-automated container terminal, simulation and comparative results demonstrate the quality advantage of the RCJ policy compared with the RNJ policy for task allocation of autonomous straddle carriers under uncertainty. Long-term testing results also show that although the auction algorithm is much more efficient than the BBCG algorithm for practical applications, it is not effective enough, even when employed by the superior RCJ policy, to achieve high-quality scheduling of autonomous SCs at the container terminals.     

Optimisation of partner selection and collaborative transportation scheduling in Virtual Enterprises using GA

Partner selection and transportation scheduling are critical to the success of a Virtual Enterprise. Collaborative transportation is a promising strategy that can help many enterprises survive and thrive in today’s highly competitive market. To help decision makers establish and operate Virtual Enterprises more effectively, an innovative decision support system is proposed in this paper. First, new model for integration of partner selection and collaborative transportation scheduling in Virtual Enterprises is developed. This integrated optimisation problem is very dynamic in nature and it is required to optimise a number of interlinked sub-problems at the same time. Then, a novel Genetic Algorithm with a unique dynamic chromosome representation and genetic operations is developed to find an optimal solution to the integrated problem. The effectiveness of the proposed approach is demonstrated in a representative case study.     

分布式NoSQL系统写操作性能优化设计与实现

分布式NoSQL系统已经成功地展现出巨大的潜力。不过在很多应用场景中,主流分布式NoSQL系统对写操作的处理效率依然不高。首先对其中存在的问题进行分析,然后提出两个优化批处理算法解决服务端批量处理的低效问题。原型系统基于典型的分布式NoSQL系统HBase实现。与官方发布的HBase系统相比,在不影响原系统一致性和持久性设计的前提下,原型系统能有效提升写操作的批量处理效率,获得10倍的吞吐量。     

Fatty acid esterification by reactive distillation: Part 2—kinetics-based design for sulphated zirconia catalysts

In a previous study on the basis of reaction equilibrium, it was demonstrated that the synthesis of fatty acids esters of heavy alcohols by catalytic reactive distillation (RD) is basically feasible. Presently, the analysis is expanded to the kinetics-based design applied to the synthesis of 2-ethylhexyl dodecanoate. Experimental data for a super-acidic sulphated zirconia catalyst are employed. It is shown that this catalyst is highly selective even for the high alcohol/acid ratios as prevail in a RD column. To avoid catalyst deactivation, liquid-liquid segregation into an organic and an aqueous phase must be prevented, and operating temperatures beyond must be chosen. A kinetic model based on liquid activities is proposed, with parameters determined from both kinetic and equilibrium measurements. A typical column design has 13 reactive stages with a catalyst loading of , temperature profile of 400-, and a space velocity of for 99.9% purity product ester. A modified Damköhler number is proposed.