Introduction to the development of an information management system for soil mix technology using artificial neural networks

This paper introduces current work in collating data from different projects using soil mix technology and establishing trends using artificial neural networks (ANNs). Variation in unconfined compressive strength as a function of selected soil mix variables (e.g., initial soil water content and binder dosage) is observed through the data compiled from completed and on-going soil mixing projects around the world. The potential and feasibility of ANNs in developing predictive models, which take into account a large number of variables, is discussed. The main objective of the work is the management and effective utilization of salient variables and the development of predictive models useful for soil mix technology design. Based on the observed success in the predictions made, this paper suggests that neural network analysis for the prediction of properties of soil mix systems is feasible. © ASCE 2011.

Soil mix technology for land remediation: Recent innovations

The past 15 years have seen increasing applications of soil mix technology in land remediation, mainly in stabilisation/solidification treatments and the construction of low-permeability cut-off walls and permeable reactive barriers; clear evidence of the versatility of the technology and its wide-ranging applications. This paper provides an overview of some of the recent innovations of soil mix technology in land remediation covering equipment developments and applications, including systems for rectangular panels and trenching systems, treatments, such as chemical oxidation, and additives, such as modified clays, zeolites and reactive magnesia. The paper also provides case studies for such innovations. The paper concludes with an overview of an on-going research and development project SMiRT (Soil Mix Remediation Technology) which will involve field trials on a contaminated site and will employ some of the innovations discussed in the paper. The range of significant advantages that soil mix technology now offers compared to other remediation techniques is likely to place this remediation method at the forefront of remedial options for future brownfield projects.

Metal Retention Experiments for the Design of Soil-Mix Technology Permeable Reactive Barriers

Soil-mix technology is effective for the construction of permeable reactive barriers (PRBs) for in situ groundwater treatment. The objective of this study was to perform initial experiments for the design of soil-mix technology PRBs according to (i) sorption isotherm, (ii) reaction kinetics and (iii) mass balance of the contaminants. The four tested reactive systems were: (i) a granular zeolite (clinoptilolite-GZ), (ii) a granular organoclay (GO), (iii) a 1:1-mixture GZ and model sandy clayey soil and (iv) a 1:1:1-mixture of GZ, GO and model soil. The laboratory experiments consisted of batch tests (volume 900mL and sorbent mass 18g) with a multimetal solution of Pb, Cu, Zn, Cd and Ni. For the adsorption experiment, the initial concentrations ranged from 0.01 to 0.5mM (2.5 to 30mg/L). The maximum metal retention was measured in a batch test (300mg/L for each metal, volume 900mL, sorbent mass 90-4.5g). The reactive material efficiency order was found to be GZ>GZ-soil mix>GZ-soil-GO mix>GO. Langmuir isotherms modelled the adsorption, even in presence of a mixed cations solution. Adsorption was energetically favourable and spontaneous in all cases. Metals were removed according to the second order reaction kinetics; GZ and the 1:1-mix were very similar. The maximum retention capacity was 0.1-0.2mmol/g for Pb in the presence of clinoptilolite; for Cu, Zn, Cd and Ni, it was below 0.05mmol/g for the four reactive systems. Mixing granular zeolite, organoclay and model soil increased the chemisorption. Providing that GZ is reactive enough for the specific conditions, GZ can be mixed to obtain the required sorption. Granular clinoptilolite addition to soil is recommended for PRBs for metal contaminated groundwater. The laboratory experiments consisted of batch tests with a multimetal solution of Pb, Cu, Zn, Cd and Ni. The four reactive materials chosen were granular zeolite, clinoptilolite and model sandy clayey soil, granular organoclay and a mix of clinoptilolite, model soil and organoclay. The reactive material efficiency order was found to be granular clinoptilolite>clinoptilolite-soil mix>clinoptilolite-soil-organoclay mix>granular organoclay. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Exploring the impacts of the interactions between lifecycles and other dynamics that influence the development of technology-based industries

To address future uncertainty within strategy and innovation, managers extrapolate past patterns and trends into the future. Several disciplines make use of lifecycles, often with a linear sequence of identified phases, to make predictions and address likely uncertainties. Often the aggregation of several cycles is then interpreted as a new cycle - such as product lifecycles into an industry lifecycle. However, frequently different lifecycle terms - technology, product, industry - are used interchangeably and without clear definition. Within the interdisciplinary context of technology management, this juxtaposition of dynamics can create confusion, rather than clarification. This paper explores some typical dynamics associated with technology-based industries, using illustrative examples from the automotive industry. A wide range of dimensions are seen to influence the path of a technology-based industry, and stakeholders need to consider the likely causality and synchronicity of these. Some curves can simply present the aggregation of components; other dynamics incur time lags, rather than being superimposed, but still have a significant impact. To optimise alignment of the important dimensions within any development, and for future strategy decisions, understanding these interactions will be critical. © 2011 IEEE.