The green state and industrial decarbonisation

ABSTRACT

The large share of carbon emitted by energy-intensive industries in the extraction and processing of basic materials must be limited to decarbonise society and the economy. Ways in which the state can govern industrial decarbonisation and contributes to green state theory are explored by addressing a largely ignored issue: the green state’s industrial relations and its role in industrial governance. With insights from a Swedish case study, the tension between the state’s economic imperative and ecological concerns in greening industry are shown to persist. However, as the energy-intensive industry’s previously privileged position in the economy is weakening, industry is opened to decarbonisation strategies. While the case exposes a number of governance challenges, it also suggests potential areas where the state can pursue decarbonisation in energy-intensive industry and points the way to an active role of the green state in governing industrial decarbonisation and greening industry.     

Testing environment Kuznets curve (EKC) hypothesis in different regions

Environmental Science and Pollution Research - This study examines the association between air pollution and economic growth based on the idea of environment Kuznets curve (EKC) hypothesis which...     

Improving of photocatalytic activity of barium ferrate via bismuth and copper co-doping for degradation of paracetamol under visible light irradiation

Nanosized Ba_{1- x}Bi_xFe_1-xCu_xO₃（12–50 nm） with x values of 0, 0.01, 0.05, and 0.1 system was prepared using the Pechini method. Structural, morphological, surface and optical characterizations were performed for the prepared samples. Cubic phase was the predominant phase for the undoped BaFeO₃ and Bi and Cu co-doped BaFeO₃ samples. Minor phases of monoclinic Ba₂Fe₂O₅, orthorhombic BaFe     

Explosibility of micron- and nano-size titanium powders

Explosibility of micron- and nano-titanium was determined and compared according to explosion severity and likelihood using standard dust explosion equipment. ASTM methods were followed using a Siwek 20-L explosion chamber, MIKE 3 apparatus and BAM oven. The explosibility parameters investigated for both size ranges of titanium include explosion severity (maximum explosion pressure (P_max) and size-normalized maximum rate of pressure rise (K_St)) and explosion likelihood (minimum explosible concentration (MEC), minimum ignition energy (MIE) and minimum ignition temperature (MIT)). Titanium particle sizes were ?100 mesh (<150 μm), ?325 mesh (<45 μm), ≤20 μm, 150 nm, 60–80 nm, and 40–60 nm. The results show a significant increase in explosion severity as the particle size decreases from ?100 mesh with an apparent plateau being reached at ?325 mesh and ≤20 μm. Micron-size explosion severity could not be compared with that for nano-titanium due to pre-ignition of the nano-powder in the 20-L chamber. The likelihood of an explosion increases significantly as the particle size decreases into the nano range. Nano-titanium is very sensitive and can self-ignite under the appropriate conditions. The explosive properties of the nano-titanium can be suppressed by adding nano-titanium dioxide to the dust mixture. Safety precautions and procedures for the nano-titanium are also discussed.     

Quantifying the effect of strong ignition sources on particle preconditioning and distribution in the 20-L chamber

Computational fluid dynamics is used to investigate the preconditioning aspect of overdriving in dust explosion testing. The results show that preconditioning alters both the particle temperature and distribution prior to flame propagation in the 20-L chamber. A parametric study gives the fluid pressure and temperature, and particle temperature and concentration at an assumed flame kernel development time (10 ms) for varying ignitor size and particle diameter. For the 10 kJ ignitor with 50% efficiency, polyethylene particles under 50 μm reach 400 K and may melt prior to flame propagation. Gases from the ignitor detonation displace the dust from the center of the chamber and may increase local particle concentration up to two times the nominal value being tested. These effects have important implications for explosive testing of dusts in the 20-L chamber and comparing to larger 1-m³ testing, where these effects may be negligible.     

Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network

Among the various techniques used for safety analysis of process systems, bow-tie (BT) analysis is becoming a popular technique as it represents an accident scenario from causes to effects. However, the BT application in the dynamic safety analysis is limited due to the static nature of its components, i.e. fault tree and event tree. It is therefore difficult in BT to take accident precursors into account to update the probability of events and the consequent risk. Also, BT is unable to represent conditional dependency. Event dependency is common among primary events and safety barriers. The current paper illustrates how Bayesian network (BN) helps to overcome these limitations. It has also been shown that BN can be used in dynamic safety analysis of a wide range of accident scenarios due to its flexible structure. This paper also introduces the application of probability adapting in dynamic safety analysis rather than probability updating. A case study from the U.S. Chemical Safety Board has been used to illustrate the application of both BT and BN techniques, with a comparison of the results from each technique.     

Precursor-based hierarchical Bayesian approach for rare event frequency estimation: A case of oil spill accidents

Due to a scarcity of data, the estimate of the frequency of a rare event is a consistently challenging problem in probabilistic risk assessment (PRA). However, the use of precursor data has been shown to help in obtaining more accurate estimates. Moreover, the use of hyper-priors to represent prior parameters in the hierarchical Bayesian approach (HBA) generates more consistent results in comparison to the conventional Bayesian method. This study proposes a framework that uses a precursor-based HBA for rare event frequency estimation. The proposed method is demonstrated using the recent BP Deepwater Horizon accident in the Gulf of Mexico. The conventional Bayesian method is also applied to the same case study. The results show that the proposed approach is more effective with regards to the following perspectives: (a) using the HBA in the proposed framework provides an opportunity to take full advantage of the sparse data available and add information from indirect but relevant data; (b) the HBA is more sensitive to changes in precursor data than the conventional Bayesian method; and (c) using hyper-priors to represent prior parameters, the HBA is able to model the variability that can exist among different sources of data.     

Magnetic particles precipitated onto wheat husk for removal of methyl blue from aqueous solution

Magnetic particles prepared via co-precipitation and impregnated onto wheat husk (MN-WH) were used for the removal of methyl blue (MB) from aqueous solution. Experiments were conducted in a batch mode for optimization regarding pH, contact time, adsorbent dose, initial dye concentrations, and temperature. Maximum adsorption (98%) was achieved at pH 5. The adsorption data were fitted into pseudo-first, pseudo-second, intraparticle diffusion, and Elovich equation revealing that adsorption followed pseudo-second-order kinetics. The four most common isotherm models, i.e. the Langmuir, Freundlich, Tempkin, and Dubinin–Radushkevich (D–R), were used to evaluate the data, with the best fit to a Langmuir isotherm (R² = 0.996), followed by a Freundlich isotherm (R² = 0.995), indicating monolayer adsorption of MB on the surface of MN-WH. Thermodynamic parameters calculated from the Van't Hoff equation revealed that the adsorption is exothermic (ΔHº = ?19.7 kJ mol^?1).     

Cleaning of water contaminated by heavy metals using beetroot fibers as biofilter

We report for the first time the use of beetroot fibers to eliminate heavy metals from polluted water. This biomass is used to remove lead, copper and zinc ions. The kinetics and beetroot fibers fixation capacities of lead, zinc and copper according to various physico‐chemical parameters such as pH, and the concentration of metal solutions were studied. The experiments were carried out in a laboratory column with fixed bed. For kinetic studies, samples of metal solutions were taken at regular intervals and measured by atomic absorption spectrometry. pH at 6.6 shows a maximum retention of the above cited metals by beetroot fibers (98%, 92%, 90% respectively). Quantity of cations retained by this biomass in simple and mixture solution is respectively estimated to be 23.6, 14.02 and 14.64 mg/g. Results indicate the usefulness of this biomass in the remediation of water contaminated by heavy metals by a rapid, practical and efficient method.