Mattia Vallerio

Solvay Specialty Polymers · Department of Chemistry

Batch processes show several sources of variability, from raw materials' properties to initial and evolving conditions that change during the different events in the manufacturing process. In this chapter, we will illustrate with an industrial example how to use machine learning to reduce this apparent excess of data while maintaining the relevant...

In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to start with examples that are irrelevant to process engineers (e.g. classification of images between cats and dogs, house pricing, types of flowers, etc.). However, process engineering principles are also based on pseudo-empirical correlations and heuristi...

Model‐based optimization techniques play a key role in achieving a sustainable operation of biochemical processes. Models are an approximation of the real process under study, hence, uncertainty is inherently present and for a sustainable process operation this uncertainty should be accounted for. In practice, optimality with respect to different c...

Nonlinear model predictive control (NMPC) has become an important tool for optimization based control of many (bio)chemical systems. A requirement for a wellperforming NMPC implementation is obtaining and maintaining an appropriate mathematical process model. To cope with model degradation in view of plant changes and/or system evolution, developme...

Nonlinear model predictive control (NMPC) has become an important tool in the control and optimization of nonlinear systems in a variety of engineering applications. A requirement for a well-performing NMPC implementation is obtaining and maintaining an appropriate mathematical model of the considered system. For linear dynamic systems, development...

In spite of the wide spread use of Nonlinear Model Predictive Control (NMPC) in large chemical companies, the small and medium enterprises (SMEs) remain oblivious of its potential mostly due to the large investment costs and in-house expertise required. This paper presents an open source python based simulation environment-SolACE, which can aid SME...

Dynamic optimization solutions largely rely on the accuracy of the underlying mathematical models. However, these models only represent an approximation of the real dynamic process and their predictions are dependent on a set of parameter values. These parameter values can be hard to estimate exactly (e.g., thermal conductivity) or vary over time (...

The manufacturing industry is faced with the challenge to constantly improve its processes under more and more stringent conditions, e.g., due to more strict environmental policies, lower profit margins and increased societal awareness. These three aspects are considered as the pillars of sustainable development and typically give rise to multiple...

A wide range of problems arising from real world applications present multi- ple and conflicting objectives to be simultaneously optimized. However, this multi- objective nature is too often neglected. Multi-objective optimization proved to be a powerful tool to correctly describe the trade-o↵s among conflict- ing objectives in a set of optimal sol...

Dynamic optimization techniques for complex nonlinear systems can provide the process industry with sustainable and efficient operating regimes. The problem with these regimes is that they usually lie close to the limits of the process. It is therefore paramount that these operating conditions are robust with respect to the parameter uncertainties...

The manufacturing industry is faced with the challenge to constantly improve its processes, e.g., due to lower profit margins, more strict environmental policies and increased societal awareness. These three aspects are considered as the pillars of sustainable development and typically give rise to multiple and conflicting objectives. Hence, any de...

Economic Model Predictive Control (EMPC) is an advanced receding horizon based control technique which optimizes an economic objective subject to potentially nonlinear dynamic equations as well as control and state constraints. The main contribution of this paper is an algorithmic differentiation (AD) based real-time EMPC algorithm including a soft...

Many practical optimal control problems (e.g., (Nonlinear) Model Predictive Control ((N)MPC)) involve multiple and conflicting objectives. Most often, this multi-objective nature is tackled by combining all individual objectives into a global weighted sum. However, selecting appropriate weights is a non-trivial task, especially when no price inform...

This paper presents a study on the model based optimisation of an industrial tubular reactor for the production of low-density polyethylene (LDPE). First a detailed reactor simulator is presented. Second, a well-posed optimisation problem is formulated. To this end, an economic cost function consisting of conversion and energy terms is derived and...

Traditionally, high-grade polysilicon is produced in the so-called ‘Siemens reactor’. The process is based on the Chemical Vapor Deposition (CVD) of silicon from a gaseous mixture of silanes and hydrogen on silicon rods. To obtain the crystal growth on the rod surface high temperatures are needed. The rods are heated internally by the Joule effect,...

A wide range of optimal control problems present multiple and conflicting objectives that need to be optimized at the same time. However, this multi-objective nature is most often neglected and typically it is tackled by constructing a global objective function consisting of a Weighted Sum (WS) of the single objectives. Unfortunately, this approach...

Nonlinear Model Predictive Control (NMPC) is a powerful technique that can be used to control many industrial processes. Different and often conflicting control objectives, e.g., reference tracking, disturbance rejection and minimum control effort, are typically present. Most often these objectives are translated into a single weighted sum (WS) obj...

The model-based optimization of the cooling system of an industrial tubular reactor for the production of low- density polyethylene (LDPE) is studied in this paper. First a detailed reactor simulator is presented. Second, a series of well- posed optimization problems are formulated and solved. To this end, the optimization problems are presented wi...

Having the possibility to systematically evaluate objectives of different nature at the same time is becoming crucial to operate processes, plants and production sites under more sustainable conditions. Most often, this multi-objective nature is tackled by combining all individual objectives into a global Weighted Sum (WS). This approach is widespr...

Many practical chemical engineering problems involve the determination of optimal trajectories given multiple and conflicting objectives. These conflicting objectives typically give rise to a set of Pareto optimal solutions. To enhance real-time decision making efficient approaches are required for determining the Pareto set in a fast and accurate...

Production of polysilicon plays a key role in the development of hi-tech and renewable energy industry. Massive production is obtained by chemical vapor deposition (CVD) in semi-batch reactors, traditionally called Siemens reactors, where silicon rods are grown. Following recent increase in market demand for polysilicon, a fine process control on i...

Export Date: 5 March 2014, Source: Scopus