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This paper proposes a novel batch process monitoring method called adjoined time series principal component analysis (AdTsPCA). In this method, a modi ed GG clustering is used for phase identi cation and data segmentation and multiple time-ordered overlapping PCA models are constructed from the data segments. The PCA models are then used for statistical process monitoring. The key characteristic of AdTsPCA is that additional information contained in the order of PCA models allows for additional diagnosis by the comparison of known process phase and suspected abnormal situation. The proposed AdTsPCA is applied to an industrial penicillin fermentation process to illustrate the e ectiveness of the method. AdTsPCA is able to detect faults in the process and signi cantly reduces the number of false positive errors in the process monitoring.

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This work investigated the bio-oil production from oil palm empty fruit bunch (EFB) by continuous pyrolysis reactor under nitrogen and steam atmospheres as sweeping gas. The study parameters were particle size, biomass feeding rate, reactor temperature, and reactor sweeping gas. The EFB particle ranges were below 500 micrometers, between 500 -1180 micrometers and 1180 -2230 micrometers. Feeding rates were 150, 350, and 550 rpm. Both factors were analyzed by single factor ANOVA. Additionally, Box-Behnken design was used to investigate temperature (350˚C -600˚C) under the following nitrogen and steam flow rates as sweeping gas: 0, 100, and 200 cm 3 /min of nitrogen and 0, 9, and 18 cm 3 /min of steam. The mathematical model from Box-Behnken design succeeded in predicting the optimal conditions for normal and nitrogen atmospheres. A particle size below 1180 µm was determined to be optimal for bio-oil production. In a normal atmosphere or no sweeping gas, the condition was 475˚C and 450 rpm of feed rate. The optimal condition for nitrogen atmosphere was 530˚C, 450 rpm of feed rate, and 200 cm 3 /min of nitrogen flow rate. However, steam as sweeping gas caused high uncertainty and the model was unable to predict the optimal conditions accurately. The bio-oils from normal, nitrogen, steam, and mixed atmospheres were analyzed for general characteristics. NMR and GC-MS were used to analyze chemical compositions in the bio-oils. Relationships between physical and chemical characteristics were determined and discussed.