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Proper simulation and identification of the flow potential of a gas storage plant is only possible if the nonlinear limits related to cavern operation and an optimal strategy (set of decision rules) related to the plant operation mode are considered. An efficient operation of a storage plant is a challenging task due to the complexity of cavern rock mechanical restrictions, as well as other technical restrictions imposed on different plant devices. The scope of this paper is to cater for these challenges by defining a set of different operational strategies, i.e., sets of actions which constitute the predefined high level mechanism that allows for an economic and efficient plant operation. In this paper, one specific strategy example is described in detail. Specifically, we give simulation results and outline an optimization procedure designed to maximize the plant’s performance capabilities. The general plant model and the form of rock mechanical restrictions of cavern operation are reviewed, and the construction of the computational algorithm is analyzed.

Complex optimization of CHP plants becomes a very important issue in research and implementation, particularly in the context of increasing environmental requirements. The process in industrial CHP plants could be decomposed into several subprocesses, which could be optimized individually using dedicated solutions. The article presents the results of work of complex, multi-modular optimization project of one CHP plant located in petrochemical and refinery plant in Poland. The scope of the project is economical load dispatch optimizer aimed to increase economical profit of CHP operation, combustion optimization for boiler efficiency increase and NOX emission reduction, steam temperature advanced control for improved control quality, sootblowing optimization for reduction of steam demand for sootblowing process. The solution includes also measurement validation and correction system, which is based on data reconciliation algorithm and on-line performance monitoring system.

Gene expression profiling is one of the most explored methods for studying cancers and microarray data repositories have become a rich and important resource. The most common human cancers develop in organs that are walled by smooth muscles. The only method of sample extraction free of unintentional contamination with surrounding tissue is microdissection. Nevertheless, such an approach is implemented infrequently. In the light of the above, there is a possibility of smooth muscle contamination in a large portion of publicly available data. In this study, 2292 publicly available microarrays were analysed to develop a simple screening method for detecting smooth muscle contamination. Microarray Inspector software was used to perform the tests since it has the unique ability to use many selected genes and probesets in a single group as a tissue definition. Furthermore, the test was dataset-independent. Two strategies of tissue definition were explored and compared. The first one depended on Tissue Specific Genes Database (TiSGeD) and BioGPS web resources, which themselves were based on meta-analysis of thousands of microarrays. The second method was based on a differential gene expression analysis of a few hundred preselected arrays. The comparison of the two methods proved the latter to be superior. Among the tested samples of undefined contamination, nearly half were identified to possibly contain significant smooth muscle traces. The obtained results equip researches with a simple method of examining microarray data for smooth muscle contamination. The presented work serves as an example of how to create definitions when searching for other possible contaminations.

Microarray technology changed the landscape of contemporary life sciences by providing vast amounts of expression data. Researchers are building up repositories of experiment results with various conditions and samples which serve the scientific community as a precious resource. Ensuring that the sample is of high quality is of utmost importance to this effort. The task is complicated by the fact that in many cases datasets lack information concerning pre-experimental quality assessment. Transcription profiling of tissue samples may be invalidated by an error caused by heterogeneity of the material. The risk of tissue cross contamination is especially high in oncological studies, where it is often difficult to extract the sample. Therefore, there is a need of developing a method detecting tissue contamination in a post-experimental phase. We propose Microarray Inspector: customizable, user-friendly software that enables easy detection of samples containing mixed tissue types. The advantage of the tool is that it uses raw expression data files and analyses each array independently. In addition, the system allows the user to adjust the criteria of the analysis to conform to individual needs and research requirements. The final output of the program contains comfortable to read reports about tissue contamination assessment with detailed information about the test parameters and results. Microarray Inspector provides a list of contaminant biomarkers needed in the analysis of adipose tissue contamination. Using real data (datasets from public repositories) and our tool, we confirmed high specificity of the software in detecting contamination. The results indicated the presence of adipose tissue admixture in a range from approximately 4% to 13% in several tested surgical samples.