Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks

Data-driven adaptive compensation control for a class of nonlinear discrete-time system with bounded disturbances

This paper considers the compensation control problem for a class of nonlinear discrete-time systems subject to bounded disturbances. With the help of the dynamic linearization technique (DLT), an equivalent data model to the unknown disturbed controlled plant is first established. Based on the data model, two data-driven controllers are designed through novel disturbance-related compact-form and partial-form DLT, which are equivalent to the unknown ideal compensation controller in theory. Adaptive gains designed for the proposed controllers are time-varying and are adaptively updated by directly utilizing the I/O data without involving any model information of the controlled plant, making both controllers purely data-driven adaptive disturbance compensation controllers. Further, in practice, unmeasurable disturbances are commonly encountered due to expensive measuring instruments, unreliable performance or large lags. Therefore, both proposed control laws provide solutions for measurable disturbance (MD) and unmeasurable disturbance (UD) in a unified framework, where the time-varying adaptive gains fuse more system dynamics when disturbance is completely unknown except for some boundedness. The stability of the proposed controllers are strictly guaranteed, and their effectiveness and applicability are verified by a numerical simulation and a distillation column.

A Connectionist Model for Dynamic Economic Risk Analysis of Hydrocarbons Production Systems

This study presents a connectionist model for dynamic economic risk evaluation of reservoir production systems. The proposed dynamic economic risk modeling strategy combines evidence-based outcomes from a Bayesian network (BN) model with the dynamic risks-based results produced from an adaptive loss function model for reservoir production losses/dynamic economic risks assessments. The methodology employs a multilayer-perceptron (MLP) model, a loss function model; it integrates an early warning index system (EWIS) of oilfield block with a BN model for process modeling. The model evaluates the evidence-based economic consequences of the production losses and analyzes the statistical disparities of production predictions using an EWIS-assisted BN model and the loss function model at the same time. The proposed methodology introduces an innovative approach that effectively minimizes the potential for dynamic economic risks. The model predicts real-time daily production/dynamic economic losses. The connectionist model yields an encouraging overall predictive performance with average errors of 1.954% and 1.957% for the two case studies: cases 1 and 2, respectively. The model can determine transitional/threshold production values for adequate reservoir management toward minimal losses. The results show minimum average daily dynamic economic losses of $267,463 and $146,770 for cases 1 and 2, respectively. It is a multipurpose tool that can be recommended for the field operators in petroleum reservoir production management related decision making.

Assessing conformal thin film growth under nonstochastic deposition conditions: application of a phenomenological model of roughness replication to synthetic topographic images

In this work, a simple method to follow the evolution of the surface of thin films during growth on substrates characterised by high roughness is detailed. To account for real cases as much as possible, the approach presented is based on the hypothesis that deposition takes place under nonstochastic conditions, such as those typical of many thin film processes in industry and technology. In this context, previous models for roughness replication, which are mainly based on idealised deposition conditions, cannot be applied and thus ad hoc approaches are required for achieving quantitative predictions. Here it is suggested that under nonstochastic conditions a phenomenological relation can be proposed, mainly based on local roughening of surface, to monitor the statistical similarity between the film and the substrate during growth or, in other words, to detect changes of the bare substrate morphological profile occurring during the film growth on top. Such approximation is based on surface representation in terms of power spectral density of surface heights, derived from topographic images; in this work, such method will be tested on two separate batches of synthetic images which simulate thin films growth onto a real rough substrate. In particular, two growth models will be implemented: the first reproduces the surface profile obtained during an atomic force microscopy measurement by using a simple geometrical envelope of surface, regardless the thin film growth mechanism; the second reproduces the columnar growth expected under nonstochastic deposition conditions. It will be shown that the approach introduced is capable to highlight differences between the two batches and, in the second case, to quantitatively account for the replication of the substrate roughness during growth. The results obtained here are potentially interesting in that they account essentially for the geometrical features of the surfaces, and as such they can be applied to synthetic depositions that reproduce different thin film depositions and experimental contexts.