Given the global economic and societal importance of the polymer industry, the continuous search for improvements in the various processing techniques is of practical primordial importance. This review evaluates the application of optimization methodologies to the main polymer processing operations. The most important characteristics related to the usage of optimization techniques, such as the nature of the objective function, the type of optimization algorithm, the modelling approach used to evaluate the solutions, and the parameters to optimize, are discussed. The aim is to identify the most important features of an optimization system for polymer processing problems and define the best procedure for each particular practical situation. For this purpose, the state of the art of the optimization methodologies usually employed is first presented, followed by an extensive review of the literature dealing with the major processing techniques, the discussion being completed by considering both the characteristics identified and the available optimization methodologies. This first part of the review focuses on extrusion, namely single and twin-screw extruders, extrusion dies, and calibrators. It is concluded that there is a set of methodologies that can be confidently applied in polymer processing with a very good performance and without the need of demanding computation requirements.

The application of optimization techniques to improve the performance of polymer processing technologies is of great practical consequence, since it may result in significant savings of materials and energy resources, assist recycling schemes and generate products with better properties. The present review aims at identifying and discussing the most important characteristics of polymer processing optimization problems in terms of the nature of the objective function, optimization algorithm, and process modelling approach that is used to evaluate the solutions and the parameters to optimize. Taking into account the research efforts developed so far, it is shown that several optimization methodologies can be applied to polymer processing with good results, without demanding important computational requirements. Furthermore, within the field of artificial intelligence, several approaches can reach significant success. The first part of this review demonstrated the advantages of the optimization approach in polymer processing, discussed some concepts on multi-objective optimization and reported the application of optimization methodologies to single and twin screw extruders, extrusion dies and calibrators. This second part focuses on injection molding, blow molding and thermoforming technologies.

The aim of the present work is to investigate the effect of halloysite nanotubes (HNT) on the mechanical properties of low-density polyethylene composites modified by maleic anhydride-grafted PE (PE-graft-MA). Polyethylene nanocomposites were prepared using an injection molding machine, Arburg Allrounder 320 C 500–170; the HNT content was varied at 0 wt %, 2 wt %, 4 wt % and 6 wt %, and the PE-graft-MA content was varied at 5 wt %. The composites were examined for their ultimate tensile stress, strain at ultimate stress, hardness, impact strength, melt flow rate, heat deflection temperature, Vicat softening temperature, crystallinity degree and phase transition temperature. It was found that the addition of halloysite nanotubes to low-density polyethylene (LDPE) led to an increased heat deflection temperature (HDT, up to 47 °C) and ultimate tensile strength (up to 16.00 MPa) while the Vicat softening temperature, strain at ultimate stress, impact strength and hardness of examined specimens slightly decreased. Processing properties of the materials specified by the melt flow rate (MFR) deteriorated almost twice. The results have demonstrated that the nanoparticles can reinforce enhance LDPE at low filler content without any considerable loss of its ductility, but only when halloysite nanotubes are superbly distributed in the polyethylene matrix.

Four different plastics were tested: potato starch based plastic (TPS-P)–BIOPLAST GF 106/02; corn starch based plastic (TPS-C)–BioComp BF 01HP; polylactic acid (polylactide) plastic (PLA)—BioComp BF 7210 and low density polyethylene, trade name Malen E FABS 23-D022; as a petrochemical reference sample. Using the blown film extrusion method and various screw rotational speeds, films were obtained and tested, as a result of which the following were determined: breaking stress, strain at break, static and dynamic friction coefficient of film in longitudinal and transverse direction, puncture resistance and strain at break, color, brightness and gloss of film, surface roughness, barrier properties and microstructure. The biodegradable plastics tested are characterized by comparable or even better mechanical strength than petrochemical polyethylene for the range of film blowing processing parameters used here. The effect of the screw rotational speed on the mechanical characteristics of the films obtained was also demonstrated. With the increase in the screw rotational speed, the decrease of barrier properties was also observed. No correlation between roughness and permeability of gases and water vapor was shown. It was indicated that biodegradable plastics might be competitive for conventional petrochemical materials used in film blowing niche applications where cost, recyclability, optical and water vapor barrier properties are not critical.

The article deals with the problem of detecting moisture in the walls of historical buildings. As part of the presented research, the following four methods based on mathematical modeling and machine learning were compared: total variation, least-angle regression, elastic net, and artificial neural networks. Based on the simulation data, the systems for the reconstruction of “pixel by pixel” tomographic images were trained. In order to test the reconstructive algorithms obtained during the research, images were generated based on real measurements and simulation cases. The method comparison was performed on the basis of three indicators: mean square error, relative image error, and image correlation coefficient. The above indicators were applied to four selected variants that corresponded to various parts of the walls. The variants differed in the dimensions of the tested wall sections, the number of electrodes used, and the resolution of the 3D image meshes. In all analyzed variants, the best results were obtained using the elastic net algorithm. In addition, all machine learning methods generated better tomographic reconstructions than the classic Total Variation method.

This paper presents a characterization of a plastic extrusion process and the selected properties of three biodegradable plastic types, in comparison with LDPE (low-density polyethylene). The four plastics include: LDPE, commercial name Malen E FABS 23-D022; potato starch based plastic (TPS-P), BIOPLAST GF 106/02; corn starch based plastic (TPS-C), BioComp^®BF 01HP; and a polylactic acid (polylactide) plastic (PLA), BioComp^®BF 7210. Plastic films with determined geometric parameters (thickness of the foil layer and width of the flattened foil sleeve) were produced from these materials (at individually defined processing temperatures), using blown film extrusion, by applying different extrusion screw speeds. The produced plastic films were tested to determine the geometrical features, MFR (melt flow rate), blow-up ratio, draw down ratio, mass flow rate, and exit velocity. The tests were complemented by thermogravimetry, differential scanning calorimetry, and chemical structure analysis. It was found that the biodegradable films were extruded at higher rate and mass flow rate than LDPE; the lowest thermal stability was ascertained for the film samples extruded from TPS-C and TPS-P, and that all tested biodegradable plastics contained polyethylene.