Abstract Reducing process variability is crucial to reach a more profitable operating point. Periodical disturbances, however, impose barriers to achieve this goal. Their effect can be strong since one disturbance that appears in a specific loop of a highly coupled plant can be seen in several loops. Thus, isolating their source and diagnosing their cause are essential. In this work, we describe the application of spectral independent component analysis to isolate a periodical disturbance that has a strong impact on the final variability in a polyethylene plant located in Southern Brazil. After the first analysis, the source was detected and the cause identified: valve stiction. To identify the cause (valve, bad tuning, or periodic disturbance), we used the methodology based on higher-order statistics. Once the valve problem had been overcome, the product variance was reduced by 93%.

The application of microalgae for energy purposes and CO2 biomitigation continues to present a number of challenges, including the optimization of culture conditions. The application of experimental designs for microalgae cultivation is difficult, since experiments involving such microorganisms generally last days or weeks. This work proposes a multipurpose laboratory apparatus for the optimization of microalgae experimental conditions that simultaneously enables the evaluation of variables such as temperature, irradiance, photoperiod and CO2 concentration in the aeration stream, as well as variables related to the concentration of culture media nutrients. A case study is also presented in which temperature, concentration of f/2 medium sodium nitrate and the effects of incident light intensity on Nannochloropsis oculata lipid content are evaluated. Experiments were carried out following central composite designs, in batch cultivation within an airlift photobioreactor apparatus. The best experimental result was obtained at 21 ºC, 119 mg/L NaNO3 and 137 µE.m-2.s-1, corresponding to 41.8% lipids and 211.9 mg.L-1 final lipid concentrations.

In biotechnological processes, the productivity and costs depend strongly on the control of the operating conditions. For this reason, sensors that allow the monitoring of variables of interest become quite important. 2D fluorescence spectroscopy is one promising option among those that are being applied for this purpose. In the present work, three methods were evaluated to select the best excitation/emission wavelength pairs of 2D fluorescence spectra to infer product, substrate and cellular concentrations throughout a fermentation using a multiple linear chemometric model: Exhaustive Search (ES), Stepwise Regression and Genetic Algorithm (GA). The Stepwise Regression presented unsatisfying results, while GA always led to good R² values in short computational times. However, for the proposed problem, the ES showed the best performance, finding the global optimum in a few minutes.