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%.

A well-evaluated state covariance matrix avoids error propagation due to divergence issues and, thereby, it is crucial for a successful state estimator design. In this paper we investigate the performance of the state covariance matrices used in three unconstrained Extended Kalman Filter (EKF) formulations and one constrained EKF formulation (CEKF). As benchmark case studies we have chosen: a) a batch chemical reactor with reversible reactions whose system model and measurement are such that multiple states satisfy the equilibrium condition and b) a CSTR with exothermic irreversible reactions and cooling jacket energy balance whose nonlinear behavior includes multiple steady-states and limit cycles. The results have shown that CEKF is in general the best choice of EKF formulations (even if they are constrained with an ad hoc clipping strategy which avoids undesired states) for such case studies. Contrary to a clipped EKF formulation, CEKF incorporates constraints into an optimization problem, which minimizes the noise in a least square sense preventing a bad noise distribution. It is also shown that, although the Moving Horizon Estimation (MHE) provides greater robustness to a poor guess of the initial state, converging in less steps to the actual states, it is not justified for our examples due to the high additional computational effort.

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.

This work explores a two-step, complexity reducing methodology, to analyze heat integration opportunities of an existing Olefins Plant, identify and quantify reduction of energy consumption, and propose changes of the existing heat exchanger network to achieve these goals. Besides the analysis of plant design conditions, multiple operational scenarios were considered to propose modifications for handling real plant operation (flexibility). On the strength of plant complexity and large dimension, work methodology was split into two parts: initially, the whole plant was evaluated with traditional Pinch Analysis tools. Several opportunities were identified and modifications proposed. Modifications were segregated to represent small and independent portions of the original process. One of them was selected to be re-analyzed, considering two scenarios. Reduction of problem dimension allowed mathematical methodologies (formulation with decomposition, applying LP, MILP and NLP optimization methods) to synthesize flexible networks to be applied, generating a feasible modification capable of fulfilling the proposed operational scenarios.

In this work an enzymatic lactose biosensor composed of the immobilized enzymes b-galactosidase and glucose oxidase was developed. Oxygen consumption during the reaction catalyzed by these enzymes was detected. The biosensor was integrated into an FIA (flow injection analysis) system that allows measurement of lactose on-line in less than three minutes. This biosensor was used to monitor lactose concentration during the production of b-galactosidase by the yeast Kluyveromyces marxianus from cheese whey. The sensor showed good stability after four months and after almost 7000 measurements had been performed. The analytical curve was linear in the range of lactose concentration from 1 to 30 g/L.