摘要:
It is possible to eliminate fluctuation in the pellet strength of the entrapping immobilization pellets according to production lots, and constantly and stably produce the entrapping immobilization pellets having high pellet strength regardless of the production lots. An apparatus for producing entrapping immobilization pellets by polymerizing an immobilizing material into a gel in the presence of an activated sludge to entrap and immobilize microorganisms in the immobilizing material, the apparatus including: a line mixer which mixes an activated sludge with an immobilizing material to prepare a raw material solution, a temperature sensor which measures a temperature of the prepared raw material solution, an addition pump which adds a polymerization initiator to the prepared raw material solution, and a controller which controls the addition pump according to the measured temperature to control an additive rate of the polymerization initiator.
摘要:
The hydrogen-producing method of the an aspect of the present invention is a method for producing hydrogen in which hydrogen is produced from an organic matter using a microorganism, characterized by using pellets on which hydrogen-producing acid-resistant bacteria are entrapped and immobilized, producing hydrogen by bringing the pellets into contact and react with the organic matter in an environment of a pH of 4 to 6. The inventors of the present invention have obtained a finding that a hydrogen-producing bacteria are entrapped and immobilized, so that the optimum pH of the hydrogen-producing bacterium shifts to low range. The present invention is made based on the above finding and uses pellets on which the hydrogen-producing bacteria are entrapped and immobilized, so that the hydrogen-producing bacterium is activated at a low pH range of 4 to 6 to produce hydrogen. Therefore, the effects of contaminated bacteria that consume hydrogen are less, and the yield of hydrogen can be improved.
摘要:
A large amount of entrapping immobilization pellets with highly stable quality are produced inexpensively by high-speed treatment. There is provided a process for producing entrapping immobilization pellets in which microorganisms are entrapped and immobilized in an immobilizing agent, the process comprising polymerizing a mixture containing the microorganisms and a solution of the immobilizing agent in a forming frame into a gel to prepare a pellet block.
摘要:
It is possible to eliminate fluctuation in the pellet strength of the entrapping immobilization pellets according to production lots, and constantly and stably produce the entrapping immobilization pellets having high pellet strength regardless of the production lots. An apparatus for producing entrapping immobilization pellets by polymerizing an immobilizing material into a gel in the presence of an activated sludge to entrap and immobilize microorganisms in the immobilizing material, the apparatus including: a line mixer which mixes an activated sludge with an immobilizing material to prepare a raw material solution, a temperature sensor which measures a temperature of the prepared raw material solution, an addition pump which adds a polymerization initiator to the prepared raw material solution, and a controller which controls the addition pump according to the measured temperature to control an additive rate of the polymerization initiator.
摘要:
A process for storing entrapping immobilization pellets in which microorganisms are entrapped and immobilized in an immobilizing material until the entrapping immobilization pellets are used in a treatment tank, the process comprising: storing a large pellet block in water at 15° C. or less or in air at a relative humidity of 90% or more and a temperature of 15° C. or less until the pellet block is cut into the entrapping immobilization pellets and used.
摘要:
The hydrogen-producing method of the an aspect of the present invention is a method for producing hydrogen in which hydrogen is produced from an organic matter using a microorganism, characterized by using pellets on which hydrogen-producing acid-resistant bacteria are entrapped and immobilized, producing hydrogen by bringing the pellets into contact and react with the organic matter in an environment of a pH of 4 to 6. The inventors of the present invention have obtained a finding that a hydrogen-producing bacteria are entrapped and immobilized, so that the optimum pH of the hydrogen-producing bacterium shifts to low range. The present invention is made based on the above finding and uses pellets on which the hydrogen-producing bacteria are entrapped and immobilized, so that the hydrogen-producing bacterium is activated at a low pH range of 4 to 6 to produce hydrogen. Therefore, the effects of contaminated bacteria that consume hydrogen are less, and the yield of hydrogen can be improved.
摘要:
The present invention employs data processing systems to handle debt collection by formulation the collections process as a Markov Decision Process with constrained resources, thus making it possible automatically to generate an optimal collections policy with respect to maximizing long-term expected return throughout the course of a collections process, subject to constraints on the available resources possibly in multiple organizations. This is accomplished by coupling data modeling and resource optimization within the constrained Markov Decision Process formulation and generating optimized rules based on constrained reinforcement learning process comprising applied on the basis of past historical data.
摘要:
A method of marketing optimization with respect to brand lifetime management formulates a problem of brand equity maximization utilizing Markov Decision Process (MDP) thereby casting brand equity management as a long term regard optimization problem in MDP, The marketing mix is optimized by formulating the mix as actions in MDP and, utilizing historical marketing and transaction data, aspects of the MDP are estimated.
摘要:
Outlier detection methods and apparatus have light computational resources requirement, especially on the storage requirement, and yet achieve a state-of-the-art predictive performance. The outlier detection problem is first reduced to that of a classification learning problem, and then selective sampling based on uncertainty of prediction is applied to further reduce the amount of data required for data analysis, resulting in enhanced predictive performance. The reduction to classification essentially consists in using the unlabeled normal data as positive examples, and randomly generated synthesized examples as negative examples. Application of selective sampling makes use of an underlying, arbitrary classification learning algorithm, the data labeled by the above procedure, and proceeds iteratively. Each iteration consisting of selection of a smaller sub-sample from the input data, training of the underlying classification algorithm with the selected data, and storing the classifier output by the classification algorithm. The selection is done by essentially choosing examples that are harder to classify with the classifiers obtained in the preceding iterations. The final output hypothesis is a voting function of the classifiers obtained in the iterations of the above procedure.
摘要:
Feature importance information available in a predictive model with correlation information among the variables is presented to facilitate more flexible choices of actions by business managers. The displayed feature importance information combines feature importance information available in a predictive model with correlational information among the variables. The displayed feature importance information may be presented as a network structure among the variables as a graph, and regression coefficients of the variables indicated on the corresponding nodes in the graph. To generate the display, a regression engine is called on a set of training data that outputs importance measures for the explanatory variables for predicting the target variable. A graphical model structural learning module is called that outputs a graph on the explanatory variables of the above regression problem representing the correlational structure among them. The feature importance measure, output by the regression engine, is displayed for each node in the graph, as an attribute, such as color, size, texture, etc, of that node in the graph output by the graphical model structural learning module.