An overview of waste crime, its characteristics, and the vulnerability of the EU waste sector

While waste is increasingly viewed as a resource to be globally traded, increased regulatory control on waste across Europe has created the conditions where waste crime now operates alongside a legitimate waste sector. Waste crime,is an environmental crime and a form of white-collar crime, which exploits the physical characteristics of waste, the complexity of the collection and downstream infrastructure, and the market opportunities for profit. This paper highlights some of the factors which make the waste sector vulnerable to waste crime. These factors include new legislation and its weak regulatory enforcement, the economics of waste treatment, where legal and safe treatment of waste can be more expensive than illegal operations, the complexity of the waste sector and the different actors who can have some involvement, directly or indirectly, in the movement of illegal wastes, and finally that waste can be hidden or disguised and creates an opportunity for illegal businesses to operate alongside legitimate waste operators. The study also considers waste crime from the perspective of particular waste streams that are often associated with illegal shipment or through illegal treatment and disposal. For each, the nature of the crime which occurs is shown to differ, but for each, vulnerabilities to waste crime are evident. The paper also describes some approaches which can be adopted by regulators and those involved in developing new legislation for identifying where opportunities for waste crime occurs and how to prevent it.

Assessing the occupational risk of dust particles in the Kenyan tanning industry using rapid image processing and microscopy techniques

The purpose of this study was to investigate the occupational hazards within the tanning industry caused by contaminated dust. A qualitative assessment of the risk of human exposure to dust was made throughout a commercial Kenyan tannery. Using this information, high-risk points in the processing line were identified and dust sampling regimes developed. An optical set-up using microscopy and digital imaging techniques was used to determine dust particle numbers and size distributions. The results showed that chemical handling was the most hazardous (12 mg m(-3)). A Monte Carlo method was used to estimate the concentration of the dust in the air throughout the tannery during an 8 h working day. This showed that the high-risk area of the tannery was associated with mean concentrations of dust greater than the UK Statutory Instrument 2002 No. 2677. stipulated limits (exceeding 10 mg m(-3) (Inhalable dust limits) and 4 mg m(-3) (Respirable dust limits). This therefore has implications in terms of provision of personal protective equipment (PPE) to the tannery workers for the mitigation of occupational risk.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.