| Title | Block, David - Understanding Strategies for Efficient Cleaning and Water Reuse and Their Economics |
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| File Information | Bioprocess Optimization Based On Historical Data and Artificial Neural Networks For all established biological processes, a wealth of data exist, whether in written form, electronic media, or in anecdotal form via process operators. Many times this data remains an under-utilized resource because efficient methods of capturing the knowledge represented by this data do not exist. By developing computational methods to examine archival data, it should be possible to use the natural and planned variations in past processing to optimize future fermentations. The methods developed in our lab will provide a way of identifying and repeating beneficial behavior while avoiding conditions that favor poor productivity or quality, thereby fully utilizing the knowledge-base for the process. Traditional experimental optimization methods such as factorial design experimentation and response surface methodology at production-scale are limited by high cost and equipment availability, as well as regulatory issues in the case of biopharmaceuticals. Thus far, we have used wine processing as a model system and have generated historical data on approximately 250 lots of wine (Sauvignon blanc and Cabernet Sauvignon) over the past three vintages. We have used this data to establish neural network training methods for this type of process data and to demonstrate that we can use the trained neural networks to model fermentation kinetics, as well as chemical and sensory attributes of the finished wine. We have also adapted both gradient-type and stochastic (e.g. simulated annealing and genetic algorithm) optimization methods for use with trained neural networks. Our current research is focused on finding methods for searching large databases of information for the most critical processing inputs, as well as extending the methods established in our lab to time-dependent data and to fermentations producing recombinant protein. Technology for the Prediction and Prevention of Stuck and Sluggish Wine Fermentations Stuck and sluggish alcoholic fermentations are an important problem in the wine industry, as the residual sugar left in the wines from these fermentations poses a potential stability problem in the final product. The ultimate goal of this project is the development of methodology for prediction of the kinetics of both normal and problem wine fermentations, based upon juice characteristics and intended processing. There are two components to this project, generating data on the impact of various parameters on fermentation kinetics from defined studies that will lead to a mechanistic model of cell growth and sugar utilization and use of that information for the training of neural networks that can predict fermentation behavior. This work is part of a multi-disciplinary effort in the department along with Drs. Bisson, Butzke, and Mills. Biological Control of Plant Diseases and Methods for Efficient Process Development Fungal diseases of plants pose a serious economic problem for agriculture in California and throughout the country. Eutypa Dieback in grapevines is one of these diseases that is typically controlled by repeated application of chemical pesticides, or in extreme cases, by removal and replanting. We are collaborating with Prof. Jean VanderGheynst in Biological and Agricultural Engineering to find new methods for the production of biological control agents that may increase their efficacy in the field and novel means for applying them in the field. We are currently working with Fusarium lateritium which has been shown to be active against Eutypa lata, the causative microorganism for Eutypa Dieback. In our lab, we are focusing on finding efficient experimental optimization methods for simultaneously finding the optimal combination of media components (type and concentration), inoculum characteristics (such as strain, age, and size), and fermentation parameters (e.g. DO, pH, temperature, agitation rate, and aeration rate). To accomplish this, we are using a combination of statistical and artificial intelligence techniques. |
| Author |
Block, David : artificial neural networks, boiprocess optimization, sanitation
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| Publication Date | Mar 18, 2010 |
| Date Added | Mar 30, 2010 |
| Description | Dr David Block is on the faculty of both the Department of Viticulture & Enology and the Department of Chemical Engineering and Material Science at UC Davis. His area of expertise is in bioprocess optimization based on historical data and artificial neural networks. This is his lecture given at the 2010 Recent Advances in Viticulture & Enology symposium, "Sustainability: Minimizing Environmental Footprints", held on March 18, 2010 at UC Davis. |
| OCR Text |
David E . Block University of California , Davis
ï?¡ Understanding cleaning and the factors that affect it ï?¡ Current cleaning strategies ï?¡ An introduction to Clean - in - Place ( CIP ) ï?¡ CIP in wineries and the associated costs and benefits
ï?¡ Cleaning - remove dirt and nutrients for potential growth of contaminants ï?¡ Sanitizing - reducing the microbial population to some level ( e.g . kill / remove 99 % of microbes , leaving 1 % ) ï?¡ Sterilizing - Kill / remove all microbes to some probability
ï?¡ Chemistry ï?¡ Temperature Things that can be controlled ï?¡ Contact time ï?¡ Turbulence ï?¡ Equipment design - â?쳌 cleanability â?쳌
ï?¡ Alkalies / bases ï?¡ Complex phosphates ï?¡ Surfactants ï?¡ Acids ï?¡ Chelating agents ï?¡ Proprietary combinations are available and may be better than any single class alone ï?¡ Choice will affect waste treatment ( BOD or COD )
ï?¡ Know your dirt ï?¡ High temperature may dissolve dirt better ï?¡ High temperature with protein - containing solutions may bake on equipment
ï?¡ Soak ï?§ Uses a lot of cleaner ï?§ Not much turbulence ( physical action ) ï?§ May leave dirt ring at top of tank and hard to get headplate ï?¡ High - pressure water or cleaner ï?§ Works as long as drain is large enough ï?§ Manual cleaning may still miss hard to reach spots ï?§ Lack of recycle can be costly from raw materials and waste point - of - view ï?¡ Portable recirculation loops ï?§ Takes personnel time to setup ï?§ Moving potentially hazardous chemicals ï?¡ Hand scrubbing ï?§ Requires tank entry and close contact with chemicals
Dairy Manufacturing Pharmaceuticals 1960 â?? s 1990 â?? s Wineries
Piping manifolds for supply and return CIP Fixed Unit cleaning unit Tank
ï?¡ Better cleaning - Reliability and reproducibility ï?¡ Lower costs - less personnel , time , materials , waste ï?¡ Safety - no dismantling of large equipment , contact with cleaning agents , hand scrubbing
ï?¡ Contact time ï?¡ Temperature ï?¡ Concentration / Chemistry ï?¡ Turbulence - Physical action
NaOH Water Acid Storage strainer CIP LH LH Return CLEANER Water T Dedicated Recycle Tank Steam in F LL LL Cond out F Or eductor or both â?¢ Recycle cleaning solution within a cleaning cycle â?¢ Save final rinse water for next initial rinse Drain and / or Reclamation â?¢ Reclaim water and chemicals for reuse
CIP SUPPLY TARGET TANK CIP RETURN Hook up target tank to CIP transfer panels
ï?¡ Pre - Rinse - Lowest grade of water ï?¡ Alkaline Wash - Hot ( 55 - 80 C ) ï?§ NaOH , EDTA , and Cl are common ï?¡ Post - Rinse - Water ï?¡ Acid Rinse - Ambient temperature ï?§ neutralize residual base and remove minerals ï?¡ Water Rinse Can have air blows between steps
ï?¡ Pipes and Pumps ï?§ Clean pipes at > 5 ft / s ( for turbulence ) ï?§ Clean tanks at â?ª 2 - 2.5 gpm / ft circumference â?ª 25 - 30 psig at sprayball â?ª Higher at the discharge pump ï?§ Return must bed large enough to avoid flooding
Stationary Sprayballs Stationary / Moving Nozzles
shadowing Internal heat exchange Manways Top View Ports / valves
ï?¡ Cross - contamination - mixing later and earlier steps ï?¡ Proximity to completion of processing
ï?¡ Analytics ï?§ Dirt / Product ( sensory impact molecules ) / Cleaning Agent ï?¡ Sampling ï?§ Liquid samples / swabbing ï?¡ Coverage testing - Riboflavin tests ï?§ cover - clean
Top Manway Irrigation Device Line Pumpover Side Manway Drain Positive Displacement Pump
Top Manway Sprayballs Irrigation Device Line Pumpover Side Manway CIP Supply CIP Return Positive Displacement Drain Pump
On - site nanofiltration / RO for water Re - use
ï?¡ Capital costs ï?¡ Operating costs ï?§ CIP System ï?§ Less personnel ï?§ Extra piping / valves / spray ï?§ Less water nozzles on each tank ï?§ Less chemicals ï?§ Additional engineering ï?¡ Less tangible costs ï?§ Higher quality / better ï?§ Water recovery storage cleaning and processing ï?§ Significantly safer ï?§ Smaller wastewater ï?§ Less chance for product treatment facility contamination ï?§ More sustainable ï?§ Marketing opportunity
ï?¡ Winery size / production ï?¡ Wine processing decisions that involve cleaning ï?¡ Winery location ï?¡ Availability of personnel ï?¡ Cost of water
ï?¡ Cleaning factors are time , temperature , chemistry , and turbulence ï?¡ Current methods require a lot of water , chemicals , and personnel ï?¡ CIP can reduce these requirements AND result in better cleaning ï?¡ UC Davis implementation will allow experimentation with best practices and economic analysis
ï?¡ Chik Brenneman ï?¡ Roger Boulton ï?¡ Andy Waterhouse ï?¡ All donors to the Research and Teaching Winery
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| Posted By | Bogart, Kay |
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