Due to the changes of external environment, unpredictable disturbances, etc., the industrial processes often have nonstationary characteristics. Assessment of operating performance for industrial processes with a hybrid of stationary and nonstationary variables is rather challenging since the statistical properties are time-variant. To solve this problem, a meticulous model for feature extracting to assess the operating performance of nonstationary processes is proposed in this paper. After distinguishing the stationary and nonstationary variables, the stationary variables are characterized by principal component analysis (PCA) to obtain the principal variation information, and the nonstationary variables are characterized by co-integration analysis (CA) to obtain the long term equilibrium relationship. Hence the inner features of both stationary variables and nonstationary variables are meticulously extracted. Next, to characterize the correlations between the stationary and nonstationary variables, a global model is established using the features obtained by PCA and CA. Since both types of features are stationary, the global model is built by PCA to extract the cross features of the stationary and nonstationary variables. Then an assessing strategy is developed based on the hierarchical hybrid model to online evaluate the process operation performance in detail. Finally, the application to a numerical example and a real copper flotation process illustrates the feasibility and efficiency of the proposed method.

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<p><a></a>Chromophores that absorb in the tissue-penetrant far-red/near-infrared window have long served as photocatalysts for the generation of singlet oxygen for photodynamic therapy. However, the cytotoxicity and side-reactions associated with singlet oxygen sensitization have posed a problem for using long wavelength photocatalysis to initiate other types of chemical reactions in biological environments. Described here is the use of Si-Rhodamine (SiR) dyes as photocatalysts for inducing rapid bioorthogonal chemistry using 660 nm light through the oxidation of a dihydrotetrazine to a tetrazine in the presence of trans-cyclooctene dienophiles. SiRs have been commonly used as fluorophores for applications in biology, but have not previously been applied to catalyze chemical reactions. A dihydrotetrazine/tetrazine pair is described that displays high stability in both oxidation states. A series of SiR derivatives were evaluated, and the Janelia-SiR dyes were found to be especially effective in catalyzing rapid photooxidation at low catalyst loadings (typically 1 µM). A protein that was site-selectively modified by trans-cyclooctene was quantitively conjugated upon exposure to 660 nm light and a dihydrotetrazine. By contrast, a previously described methylene blue catalyst was found to rapidly degrade the protein. SiR-red light photocatalysis was used to crosslink hyaluronic acid derivatives that were functionalized by dihydrotetrazine and trans-cyclooctenes, enabling 3D culture of human prostate cancer cells. This photoinducible hydrogel formation could also be carried out in vivo in live mice through subcutaneous injection of a solution containing SiR photocatalyst and a Cy7-labeled hydrogel precursor, followed by brief in vivo irradiation with 660 nm light to produce a stable hydrogel material. This cytocompatible method for using red light photocatalysis to activate bioorthogonal chemistry is anticipated to find broad applications where spatiotemporal control is needed in the in vivo environment. <br></p>

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A process operating performance optimality assessment (POPOA) consists of an optimal degree online assessment and non‐optimal cause identification, which contribute to maintaining a high comprehensive economic index (CEI) of the production. However, two main problems limit the application of the traditional POPOA methods, i.e., the plant‐wide process characteristics and the coexistence of both the quantitative and qualitative variables. To overcome the two problems for POPOA, a novel two‐level multi‐block assessment method based on the fuzzy probabilistic rough set (FPRS) is proposed in this research. The operating performance grade of both the global and sub‐block level are properly defined, where the sub‐block assessment indices, which are difficult to obtain, are not required. Different from traditional multi‐block methods due to the novel offline modelling method, an explicit global model is unnecessary. The global performance grade is directly determined by the sub‐block performance grades. When the process is operating at a non‐optimal performance grade, the responsible sub‐block can be rapidly identified through online assessment. The proposed non‐optimal cause identification technique is carried out in the non‐optimal sub‐blocks, based on a newly‐defined matching degree function. The identified non‐optimal causes also contribute to the actual production adjustment to obtain the optimal performance. Finally, the proposed POPOA method is successfully applied to a gold hydrometallurgy process, which is a typical plant‐wide process with hybrid types of variables.