Sustainable Value Engineering

Construction projects have struggled to attain both sustainability and value for money in the construction industry. The strengths of Value Engineering can help increase sustainability in the construction industry. Designs have been known to have “unnecessary cost.” Value Engineering is well established tool to cut unnecessary cost in project. Value Engineering comes with a host of other related advantages like risk reduction, time management, better schedule, improved quality etc. hence showing the degree of savings that can be achieved. Main barrier to the implementation of Value engineering is misconception that it increases project cost and time. If this is overcome the cost savings achieved after the Value Engineering workshop can be invested into achieving increased sustainability. Increased sustainability has its own benefits to projects like, low emissions, reduced waste, increased recycling, reuse, low running cost etc. The benefits of Value Engineering and sustainability show a strong positive correlation; hence the linking of the two to form Sustainable Value Design should produce both increased sustainability and value for money on all construction projects.

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

We have investigated the use of nanofiltration for treatment of effluent from the first alkaline extraction stage (called 'E1') of a bleached Kraft pulp plant. We report nanofiltration tests with a laboratory cross-flow unit 'CELFA P-28' to study the effect of the retentate flowrate, pH, and pressure, on permeate flowrate and pollutant removal efficiency. The data was used to predict the performance and approximate cost of a multi-stage nanofiltration unit at industrial scale. Its design was computer-optimized by the dynamic programming method.