Jiangnan University

Xiaoli Luan

Fei Liu

Zhiguo Wang

Shunyi Zhao

Haiying Wan

Niannian Zheng

Wei Wang

Jun Chen

Enbo Feng

Chenglin Liu

Gongxin Li

Zhonggai Zhao

Yuqing Ni

Xiaojing Ping

The invention provides real-time prediction and regulation methods and systems of product quality based on a process dynamic pattern. The prediction method includes: constructing a state space probability model of quality pattern dynamic motion equation; calculating a probability density function distribution of a quality pattern according to the probability model; performing optimized learning on parameters of the state space probability model of quality pattern dynamic motion equation according to the probability density function distribution, to obtain optimal model parameters and an optimized state space probability model of quality-pattern dynamic motion equation; and performing online prediction on product quality indicators based on the optimized state space probability model. The invention can implement online prediction of product quality, and perform online regulation on product quality by using the analytical relationship between the control input and the quality pattern, to ensure that the product quality remains at an optimal level.

Real-time prediction and regulation methods and systems of product quality based on process dynamic pattern

The present invention provides a knowledge reuse-based method and system for predicting a cell concentration in a fermentation process. The method includes: constructing a cell concentration soft sensor universal model in a fermentation process; acquiring and preprocessing process data of a fermentation stage A; determining a cell concentration soft sensor model of the fermentation stage A; designing a cell concentration online soft sensor of a fermentation stage B; and predicting a cell concentration of the fermentation stage B according to the cell concentration online soft sensor of the fermentation stage B. The present invention resolves the problems of weak generalization of a cell concentration soft sensor model and high costs of establishing models for fermentation stages separately, thereby improving the prediction accuracy of a cell concentration soft sensor.

Knowledge reuse-based method and system for predicting cell concentration in fermentation process

The invention provides a motion capture method of a robotic arm, including: fastening a visual sensor on a robotic arm to acquire data as a source domain, fastening an inertial sensor on a corresponding human arm to acquire data as a target domain, and establishing a state space expression of a system; setting an optimal unknown state observed joint distribution by using a total probability theory and an observed prediction distribution of the source domain as a condition, decomposing a conditional joint observed distribution model, and solving an optimal distribution using KL divergence; and transferring knowledge of the source domain measured by the visual sensor into the target domain measured by the inertial sensor based on a Kalman filter and the total probability theory, performing data fusion based on Kalman filtering, and predicting a state of the system at a next moment to implement motion capture of the robotic arm.

Motion capture method and system of robotic arm, medium, and electronic device

The disclosure discloses an apparatus and a method for microbial cell counting, and belongs to the field of cell counting. In the present application, by converting a traditional automated intermittent counting process into a continuous counting process, the cell sap fixed in a blood cell plate in a traditional counter becomes the cell sap flowing in a microchannel, so as to prolong the cell detection time and distance. The size of the microchannel is slightly greater than the diameter of microbial cells, so as to ensure that the cells flow through the cross section of the microchannel one by one. At the same time, since the diameter of the counterbores communicated by the microchannel is slightly greater than the width of the microchannel, the flow rate of the cell sap slows down when the cell sap flows to the counterbores.

Apparatus and method for microbial cell counting

The present invention discloses a multilevel pattern monitoring method for a process industry process and belongs to the fields of industrial production and processing. The multilevel pattern monitoring method comprises the steps: dividing an industry process into a plurality of levels from the view of patterns, selecting a different key performance index for each level, acquiring operating data relevant to the key performance index, identifying the pattern of each level, and proposing a pattern monitoring method for each level based on a data driven method to realize pattern monitoring in the industry process. The effect of rapidly finding a fault is achieved by monitoring real-time data according to the pattern, identified in a clustering plane, of each level, the effect of removing the fault is achieved by the selected N variables generating the greatest influence to the current pattern in combination with an expert system and an inference engine, meanwhile, the energy consumption of the process is reduced, the operating cost is optimized, and the competitiveness of a product is improved.

Multilevel pattern monitoring method for industry processes

The present invention discloses a model-free online recursive optimization method for a batch process based on variable period decomposition. Variable operation data closely related to product quality is acquired, optimization action on each subset is integrated on the basis of time domain variable division on the process by utilizing a data driving method and a global optimization strategy is formed, based on which an online recursive error correction optimization strategy is implemented. According to the method, the online optimization strategy is formed completely based on the operation data of the batch process without needing prior knowledge or a model of a process mechanism. Meanwhile, the optimized operation locus line has better adaptability by using the online recursive correction strategy, and thus the anti-interference requirement of the actual industrial production is better met.

Model-free online recursive optimization method for batch process based on variable period decomposition

The present invention relates to a non-direct measurement temperature-compensating model correction method in the on-line application of a near-infrared spectrum analyzer, which comprises: acquiring a near-infrared spectrum of each sample under different temperature levels; respectively carrying out preprocessing and principal component analysis on the acquired spectra for temperatures and to-be-measured physical property parameters; then merging the obtained spectra to generate new spectral data; using partial least squares to model the spectral data to obtain measured values at the current moment; finally, constructing an on-line recursive algorithm, and thereby on-line near-infrared measurement with a non-direct measurement temperature compensation function is fulfilled. The present invention adopts temperature as a separate latent factor variable to participate in the process of near-infrared modeling, so that when near-infrared measurement is used, by depending on the adaptability of a model to the temperature, physical property measurement under different temperatures can be carried out without direct temperature measurement information or related calculation, and thereby the created model has better universality. The on-line recursive algorithm of the present invention has better adaptability to sample temperatures and other measurement condition changes.

Method for correcting measuring-point-free temperature compensation model during online application of near infrared spectrum analyzer

The present invention discloses a data difference-driven self-learning dynamic batch process optimization method including the following steps: collect production process data off line; eliminate singular batches through PCA operation; construct time interval and index variance matrices to carry out PLS operation to generate initial optimization strategies; collect data of new batches; run a recursive algorithm; and update the optimization strategy. The present invention utilizes a perturbation method to establish initial optimization strategies for an optimized variable setting curve. On this basis, self-learning iterative updating is carried out for mean values and standard differences on the basis of differences in data statistics, so that the continuous improvement of optimized indexes is realized, and thereby a new method is provided for batch process optimization strategies for solving actual industrial problems. The present invention is fully based on operational data of a production process, and does not need priori knowledge about a process mechanism and a mechanism model. The present invention is applicable to the dynamic optimization of operation trajectories of batch reactors, batch rectifying towers, batch drying, batch fermentation, batch crystallization and other processes and systems adopting batch operation.

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Wuxi, China

Xiaoli Luan