Energy demand and efficiency measures in polymer processing: comparison between temperate and Mediterranean operating plants

Abstract

Polymer processing is an energy-intensive industry. The plastification of polymers requires a high volume of electric power for thermal energy. Electricity based power is the common form of energy in polymer processing and provides obvious potential for a reduction in energy use and costs. Measures to avoid productionbased conversion losses, total conversion and transportation losses in energy used all have social, national, economic and business relevance. A bottom-up evaluation of four different production factories in this study assesses the potential for energy use improvements. The resulting theoretical assessment suggested that reducing primary energy demand is the most powerful target for reducing energy intensity in the polymer industry followed by the introduction of improved technologies to raise energy efficiency. The transferability of the conclusions was supported by the comparison between two different geographic locations for polymer production in Germany and Western Australia. The findings of this research suggest potential in their use in 'green' decision-making in the plastics industry.

Climate Impact of Primary Plastic Production

“…Injection molding is one of the most energyintensive processes, with a process load of between 4.8 and 13 GJ per tonne, depending on hydraulic, all-electric, or hybrid injection [143,144,148,149]. About 55% of injection molding machines are hydraulic, 35% are all-electric, and 10% are hybrid [148,149].…”

“…Polymer films are typically used in packaging such as shrink, stretch, barrier films, and frozen food packaging, and shopping bags. Typical energy demand during processing ranges between 1.6 and 9.6 GJ per tonne in the literature [143,144,148,149].…”

“…After cooling, the product is cut to length, spooled, or coiled and produces a distinctive plastic channel, such as profile or tube. The energy demand of other extrusion processes varies over a large range, depending on the process and polymer, and is listed as 5.5 to 21.6 GJ per tonne in the literature [143,148,149].…”

Combining Automated Microfluidic Experimentation with Machine Learning for Efficient Polymerization Design

“…Here the microreactor was designed such that the Damköhler number can be varied between 0.3-101, enabling the sampling of both reaction rate limited and mass transport limited regimes. The Beta number varies between O(10 -3 ) and O (10) based on standard heat of polymerization of 1-hexene (III), indicating that more heat is generated than removed, enabling thermographic analysis 45,46 . Finally Reynolds number is variable between O(10 -3 ) and O(1), meaning that the reaction is operating in a laminar flow regime.…”

Combining automated microfluidic experimentation with machine learning for efficient polymerization design

“…More importantly, about 37% of all global greenhouse gasses (GHG) are created directly or indirectly by polymer manufacturing 9 . In an industry that has shaped the world as we know it and is continuing to expand rapidly, innovative solutions are needed to quickly engineer and understand new production pathways 10 . Design of laboratory-scale reactors for quick discovery and integration of machine learning for robust process understanding are two possible ways to work towards developing more efficient polymerization catalysts while minimizing environmental harm.…”