Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.

NEW PROGRESS IN MINERAL PROCESSING——REPORT ON FIRST INTERNATIONAL CONFERENCE ON MODERN PROCESS MINERALOGY AND MINERAL PROCESSING

The first International Conference on Modern Process Mineralogy and Mineral Processing, organized by the Nonferrous Metals Society of China and hosted by Beijing General Research Institute of Mining and Metallurgy,was held on September 22-25,1992,in Beijing,China.About 350 scholars and experts from 25 countries and regions showed up at the conference and 130 papers were presented,among them 98 papers are of mineral processing.Some of the papers given in mineral processing are summed up as follows.

Spatial batch optimal design based on self-learning Gaussian process models for LPCVD processes

Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.

The inversion of density structure by graphic processing unit(GPU) and identification of igneous rocks in Xisha area

Organic reefs, the targets of deep-water petro- leum exploration, developed widely in Xisha area. However, there are concealed igneous rocks undersea, to which organic rocks have nearly equal wave impedance. So the igneous rocks have become interference for future explo- ration by having similar seismic reflection characteristics. Yet, the density and magnetism of organic reefs are very different from igneous rocks. It has obvious advantages to identify organic reefs and igneous rocks by gravity and magnetic data. At first, frequency decomposition was applied to the free-air gravity anomaly in Xisha area to obtain the 2D subdivision of the gravity anomaly and magnetic anomaly in the vertical direction. Thus, the dis- tribution of igneous rocks in the horizontal direction can be acquired according to high-frequency field, low-frequency field, and its physical properties. Then, 3D forward model- ing of gravitational field was carried out to establish the density model of this area by reference to physical properties of rocks based on former researches. Furthermore, 3D inversion of gravity anomaly by genetic algorithm method of the graphic processing unit （GPU） parallel processing in Xisha target area was applied, and 3D density structure of this area was obtained. By this way, we can confine the igneous rocks to the certain depth according to the density of the igneous rocks. The frequency decomposition and 3D inversion of gravity anomaly by genetic algorithm method of the GPU parallel processing proved to be a useful method for recognizing igneous rocks to its 3D geological position. So organic reefs and igneous rocks can be identified, which provide a prescient information for further exploration.

Alunite processing method selection using the AHP and TOPSIS approaches under fuzzy environment

Alunite is the most important non bauxite resource for alumina. Various methods have been proposed and patented for processing alunite, but none has been performed at industrial scale and no technical,operational and economic data is available to evaluate methods. In addition, selecting the right approach for alunite beneficiation, requires introducing a wide range of criteria and careful analysis of alternatives.In this research, after studying the existing processes, 13 methods were considered and evaluated by 14 technical, economic and environmental analyzing criteria. Due to multiplicity of processing methods and attributes, in this paper, Multi Attribute Decision Making methods were employed to examine the appropriateness of choices. The Delphi Analytical Hierarchy Process(DAHP) was used for weighting selection criteria and Fuzzy TOPSIS approach was used to determine the most profitable candidates. Among 13 studied methods, Spanish, Svoronos and Hazan methods were respectively recognized to be the best choices.

Optimization of Formulation and Processing Process of Milk Drink Supplemented with Whole Oat Kernels

The objective of this study was to study the stability of oat kernel-supplemented milk from two perspectives of formulation and processing process,with centrifugal precipitation rate,viscosity and sensory score as evaluation indices.After optimization,the results show that the optimal compound stabilizer formula was obtained as follows:0.04%gellan gum,0.020%carrageenan,0.020%sodium carboxymethyl cellulose and 0.20%microcrystalline cellulose.With single-factor experiments and orthogonal array design,the optimal processing process that provided the best stability as well as good taste was determined as follows:batching temperature 50℃,hydration time 40 min,homogenization pressure 30 MPa and filling temperature 15℃.

MULTIVARIATE SURVIVAL DISTRIBUTIONS OF AGE AND RESIDUAL LIFETIME PROCESSES IN NONHOMOGENEOUS POISSON PROCESS

Let {V(t),t≤0} be the nonhomogeneous Poisson process with cumulative intensituy parameter A(t). |δ,t≥0 the, age process, and y, t≥0} the residual lifetime process. In the present-paper the expressions of n-dimensional survival distribution functions of the processes {δ and γ, and their Lebesgue decompositions are derived.

Progress in processing of porous titanium:a review

As a novel structural and functional material,porous titanium and its alloys have been widely used in the aerospace,marine engineering and biomedical fields due to their high corrosion resistance,low density,good biocompatibility and excellent mechanical properties.Therefore,in this paper,a comprehensive review of powder metallurgy(PM)(including additive manufacturing(AM)processes)for fabricating porous titanium is firstly covered in terms of their working principles,capabilities,shortcomings and strengths.Simultaneously,the influencing factors of various methods on final pore structure of porous Ti are involved.Secondly,a summary of the chemical methods(CM)to obtain the porous Ti is also provided,such as dealloying method and reduction method.Finally,the tendency and direction of preparation technology as well as application of porous titanium were prospected.

基于负载均衡的CPU-GPU异构计算平台任务调度策略

针对中央处理单元-图形处理单元(central processing unit-graphics processing unit,CPU-GPU)异构计算系统中,CPU和GPU负载不均导致系统性能降低的问题,提出了一种基于队列的混合调度策略.该策略通过探测获得CPU和GPU处理指定任务的计算能力,将计算任务按照探测比例分配给CPU和GPU;将并行任务存入双向队列,以降低调度带来的额外开销.结果表明,使用该策略的基准测试程序系统性能平均提升了28.07%.总体而言,该调度策略能够缩短CPU与GPU完成各自计算任务后的等待时间,有效平衡系统CPU与GPU之间的负载,提升系统性能.

Industrial shape detecting system of cold rolling strip

A high-precision shape detecting system of cold rolling strip is developed to meet industrial application, which mainly consists of the shape detecting roller, the collecting ring, the digital signal processing （DSP） shape signal processing board and the shape control model. Based on the shape detecting principle, the shape detecting roller is designed with a new integral structure for improving the precision of shape detecting and avoiding scratching strip surface. Based on the DSP technology, the DSP shape signal processing circuit board is designed and embedded in the shape detecting system for the reliability and stability of shape signal processing. The shape detecting system was successfully used in Angang 1 250 mm HC 6-high reversible cold rolling mill. The precision of shape detecting is 0.2 I and the shape deviation is controlled within 6 1 after the close loop shape control is input.

Geology of Mars after the first 40 years of exploration

The knowledge of Martian geology has increased enormously in the last 40 yr. Several missions orbiting or roving Mars have revolutionized our understanding of its evolution and geological features, which in several ways are similar to Earth, but are extremely different in many respects. The impressive dichotomy between the two Martian hemispheres is most likely linked to its impact cratering history, rather than internal dynamics such as on Earth. Mars＇ volcanism has been extensive, very longlived and rather constant in its setting. Water was available in large quantities in the distant past of Mars, when a magnetic field and more vigorous tectonics were active. Exogenic forces have been shaping Martian landscapes and have led to a plethora of landscapes shaped by wind, water and ice. Mars＇ dynamical behavior continues, with its climatic variation affecting climate and geology until very recent times. This paper tries to summarize major highlights in Mars＇ Geology, and points to deeper and more extensive sources of important scientific contributions and future exploration.

Safety analysis of wheel brake system based on STAMP/STPA and Monte Carlo simulation

The wheel brake system safety is a complex problem which refers to its technical state, operating environment, human factors, etc., in aircraft landing taxiing process. Usually, professors consider system safety with traditional probability techniques based on the linear chain of events. However, it could not comprehensively analyze system safety problems, especially in operating environment, interaction of subsystems, and human factors. Thus,we consider system safety as a control problem based on the system-theoretic accident model, the processes(STAMP) model and the system theoretic process analysis(STPA) technique to compensate the deficiency of traditional techniques. Meanwhile,system safety simulation is considered as system control simulation, and Monte Carlo methods are used which consider the range of uncertain parameters and operation deviation to quantitatively study system safety influence factors in control simulation. Firstly,we construct the STAMP model and STPA feedback control loop of the wheel brake system based on the system functional requirement. Then four unsafe control actions are identified, and causes of them are analyzed. Finally, we construct the Monte Carlo simulation model to analyze different scenarios under disturbance. The results provide a basis for choosing corresponding process model variables in constructing the context table and show that appropriate brake strategies could prevent hazards in aircraft landing taxiing.

A model of supplying poplar wood for Iranian paper & wood factories

The objective of this research is to design a method for an appropriate alternative to supply poplar wood as raw material for wood and paper factories in Iran. Due to the destruction of the forests and lack of proper plantation, replacement of the forest wood by the fast growing wood is vital to satisfy all requirements of these factories. A hierarchy was used to prioritize benefits, opportunities, costs and risks （BOCR） using the Analytic Hierarchy Process （AHP） ratings approach. To evaluate the ＂control criteria＂ of the system, a control hierarchy was also created and prioritized by applying the Analytic Network Process （ANP）. A total of four major control criteria in the system are prioritized where each one controls a network structure evaluated by using ANE The final synthesis results of the system showed that internal poplar tree farming supplied by the forest product factories was the best choice among three potential alternatives （factory procurement, external procurement and mix procurement）.

Evolution Handoff Strategy for Real-Time Video Transmission over Practical Cognitive Radio Networks

The transmission delay of realtime video packet mainly depends on the sensing time delay(short-term factor) and the entire frame transmission delay(long-term factor).Therefore,the optimization problem in the spectrum handoff process should be formulated as the combination of microscopic optimization and macroscopic optimization.In this paper,we focus on the issue of combining these two optimization models,and propose a novel Evolution Spectrum Handoff(ESH)strategy to minimize the expected transmission delay of real-time video packet.In the microoptimized model,considering the tradeoff between Primary User's(PU's) allowable collision percentage of each channel and transmission delay of video packet,we propose a mixed integer non-linear programming scheme.The scheme is able to achieve the minimum sensing time which is termed as an optimal stopping time.In the macro-optimized model,using the optimal stopping time as reward function within the partially observable Markov decision process framework,the EHS strategy is designed to search an optimal target channel set and minimize the expected delay of packet in the long-term real-time video transmission.Meanwhile,the minimum expected transmission delay is obtained under practical cognitive radio networks' conditions,i.e.,secondary user's mobility,PU's random access,imperfect sensing information,etc..Theoretical analysis and simulation results show that the ESH strategy can effectively reduce the transmission delay of video packet in spectrum handoff process.

Interpreting the dynamic effect of internal heat integration on reactive distillation columns

In this work,the impact of internal heat integration upon process dynamics and controllability by superposing reactive section onto stripping section,relocating feed locations,and redistributing catalyst within the reactive section is explored based on a hypothetical ideal reactive distillation system containing an exothermic reaction:A + BC + D.Steady state operation analysis and closed-loop controllability evaluation are carried out by comparing the process designs with and without the consideration of internal heat integration.For superposing reactive section onto stripping section,favorable effect is aroused due to its low sensitivities to the changes in operating condition.For ascending the lower feed stage,somewhat detrimental effect occurs because of the accompanied adverse internal heat integration and strong sensitivity to the changes in operating condition.For descending the upper feed stage,serious detrimental effect happens because of the introduced adverse internal heat integration and strong sensitivity to the changes in operating condition.For redistributing catalyst in the reactive section,fairly small negative influence is aroused by the sensitivity to the changes in operating condition.When reinforcing internal heat integration with a combinatorial use of these three strategies,the decent of the upper feed stage should be avoided in process development.Although the conclusions are derived based on the hypothetical ideal reactive distillation column studied,they are considered to be of general significance to the design and operation of other reactive distillation columns.

Unique influences of reboiler inventory control on the operation of totally reboiled reactive distillation columns

In this work,the dynamics and operation of the totally reboiled reactive distillation columns are visualized in terms of transfer function based process models.This kind of processes is found to be characterized by underdamped step responses due to the special topological configuration and the intricate interplay between the reaction operation and the separation operation involved.The under-dampness can be substantially alleviated through the tight inventory control of bottom reboiler and this presents beneficial effects to process dynamics and operation.Two totally reboiled reactive distillation columns,separating,respectively,a hypothetical synthesis reaction from reactants A and B to product C,and a real decomposition reaction from 1,4-butanediol to tetrahydrofuran and water,are employed to demonstrate these uncommon behaviors.The results obtained give full support to the above qualitative interpretation.Despite the strong influences of reaction kinetics and thermodynamic properties of the reacting mixtures,the totally reboiled reactive distillation columns are generally considered to present such unique behaviors and require tight inventory control of bottom reboiler to facilitate their control system development.

Learning control of fermentation process with an improved DHP algorithm

Control of the fed-batch ethanol fermentation processes to produce maximum product ethanol is one of the key issues in the bioreactor system.However,ethanol fermentation processes exhibit complex behavior and nonlinear dynamics with respect to the cell mass,substrate,feed-rate,etc.An improved dual heuristic programming algorithm based on the least squares temporal difference with gradient correction(LSTDC) algorithm(LSTDC-DHP) is proposed to solve the learning control problem of a fed-batch ethanol fermentation process.As a new algorithm of adaptive critic designs,LSTDC-DHP is used to realize online learning control of chemical dynamical plants,where LSTDC is commonly employed to approximate the value functions.Application of the LSTDC-DHP algorithm to ethanol fermentation process can realize efficient online learning control in continuous spaces.Simulation results demonstrate the effectiveness of LSTDC-DHP,and show that LSTDC-DHP can obtain the near-optimal feed rate trajectory faster than other-based algorithms.

Multifractal modeling of the production of concentrated sugar syrup crystal

High quality, concentrated sugar syrup crystal is produced in a critical step in cane sugar production： the clarification process. It is characterized by two variables： the color of the produced sugar and its clarity degree. We show that the temporal variations of these variables follow power-law distributions and can be well modeled by multiplicative cascade multifractal processes. These interesting properties suggest that the degradation in color and clarity degree has a systemwide cause. In particular, the cascade multifractal model suggests that the degradation in color and clarity degree can be equivalently accounted for by the initial ＂impurities＂ in the sugarcane. Hence, more effective cleaning of the sugarcane before the clarification stage may lead to substantial improvement in the effect of clarification.

An input-output model for energy accounting and analysis of industrial production processes： a case study of an integrated steel plant

To promote sustainability, it has become increasingly vital to properly account material and energy flows in industrial production processes. Therefore, a generic process-level input-output （IO） model was developed to provide an integrated energy （material） accounting and analysis approach for industrial production processes. By extending the existing processlevel IO models, the production, usage, export and loss of by-products were explicitly considered in the proposed IO model. Moreover, the by-products allocation procedures were incorporated into the proposed IO model to reflect individual contributions of products to energy consumption. Finally, the proposed model enabled calculating embodied energy of main products and total energy consumption under hierarchical accounting scope. Plant managers, energy management consultants, governmental officials and academic researchers could use this input-output model to account material and energy flows, thus calculating energy consumption indicators of a production process with their specific system boundary requirements. The accounting results could be further used for energy labeling, identifying bottlenecks of production activities, evaluating industrial symbiosis effects, improving materials and energy utilization efficiency, etc. The model could also be used as a planning tool to determine the effect that a particular change of technology and supply chains may have on the industrial production processes. The proposed model was tested and applied in a real integrated steel mill, which also provided the reference results for related researches. At last, some concepts, computational issues and limi- tations of the proposed model were discussed.

Ultrasonic attenuation spectroscopy for multivariate statistical process control in nanomaterial processing

Ultrasonic attenuation spectroscopy （UAS） is an attractive process analytical technology （PAT） for on-line real-time characterisation of slurries for particle size distribution （PSD） estimation. It is however only applicable to relatively low solid concentrations since existing instrument process models still cannot fully take into account the phenomena of particle-particle interaction and multiple scattering, leading to errors in PSD estimation. This paper investigates an alternative use of the raw attenuation spectra for direct multivariate statistical process control （MSPC）. The UAS raw spectra were processed using principal component analysis. The selected principal components were used to derive two MSPC statistics, the Hotelling＇s T2 and square prediction error （SPE）. The method is illustrated and demonstrated by reference to a wet milling process for processinR nanoparticles.